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Forschungspublikationen des IST

Hier finden Sie eine Liste aller Publikationen des IST.

Publikationsliste

  1. 2019

    1. F. Allgöwer et al., “Position paper on the challenges posed by modern applications to cyber-physical systems theory,” Nonlinear Analysis: Hybrid Systems, vol. 34, pp. 147–165, 2019.
    2. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Event-triggered and self-triggered control for linear systems based on reachable sets,” Automatica, vol. 101, pp. 15–26, 2019.
    3. J. Köhler, M. A. Müller, and F. Allgöwer, “Distributed model predictive control�??Recursive feasibility under inexact dual optimization,” Automatica, vol. 102, pp. 1--9, 2019.
    4. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “Periodic event-triggered control for networked control systems based on non-monotonic Lyapunov functions,” Automatica, vol. 106, pp. 35–46, 2019.
    5. F. Pfitz, M. Braun, and C. Ebenbauer, “Relaxed Barrier MPC for Reference Tracking: Theoretical and experimental studies,” VDI-Berichte, vol. 2349, pp. 97–110, 2019.
    6. A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input-output data,” IEEE Control Systems Letters, vol. 3, pp. 709--714, 2019.
    7. S. Wildhagen, M. A. Müller, and F. Allgöwer, “Predictive Control over a Dynamical Token Bucket Network,” IEEE Control Systems Letters, vol. 3, no. 4, pp. 21–26, 2019.
    8. M. Gharbi and C. Ebenbauer, “A proximity moving horizon estimator based on Bregman distances and relaxed barrier functions,” in Proc.\ 18th European Control Conference (ECC), Napoli, Italy, Australia, 2019, pp. 1790–1795.
    9. W. Halter, S. Michalowsky, and F. Allgöwer, “Extremum seeking for optimal enzyme production under cellular fitness constraints,” in Proc.\ European Control Conf.\ (ECC), Neapel, Italien, 2019.
    10. J. Köhler, M. A. Müller, and F. Allgöwer, “A simple framework for nonlinear robust output-feedback MPC,” in Proc. 18th European Control Conference (ECC), Naples, Italy, 2019, pp. 793–798.
    11. P. N. Köhler, M. A. Müller, and F. Allgöwer, “Approximate dissipativity and performance bounds for interconnected systems,” in Proc. 18th European Control Conference (ECC), Naples, Italy, 2019, pp. 787–792.
    12. S. Linsenmayer, B. W. Carbelli, F. Dürr, J. Falk, F. Allgöwer, and K. Rothermel, “Integration of Communication Networks and Control Systems Using a Slotted Transmission Classification Model,” in Proc.\ 16th IEEE Annual Consumer Communications Networking Conf.\ (CCNC), Las Vegas, NV, USA, 2019, pp. 1–6.
    13. T. Martin, P. N. Köhler, and F. Allgöwer, “Dissipativity and Economic Model Predictive Control for Optimal Set Operation,” in Proc.\ American Control Conf.\ (ACC), Philadelphia, USA, 2019, pp. 1020–1026.
    14. A. Romer, S. Trimpe, and F. Allgöwer, “Data-driven inference of passivity properties via Gaussian process optimization,” in Proc.\ European Control Conf.\ (ECC), 2019, pp. 29--35.
    15. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-Driven Model Predictive Control with Stability and Robustness Guarantees.” 2019.
    16. S. Linsenmayer and F. Allgöwer, “Control over Networks Using a Slotted Transmission Classication Model.” 2019.
  2. 2018

    1. F. A. Bayer, M. A. Müller, and F. Allgöwer, “On optimal system operation in robust economic MPC,” Automatica, vol. 88, pp. 98–106, 2018.
    2. J. Berberich, J. W. Dietrich, R. Hoermann, and M. A. Müller, “Mathematical modeling of the pituitary-thyroid feedback loop: role  of a TSH-T3-shunt and sensitivity analysis,” Frontiers in Endocrinology, 2018.
    3. J. Berberich, J. Köhler, F. Allgöwer, and M. A. Müller, “Indefinite Linear Quadratic Optimal Control: Strict Dissipativity and Turnpike Properties,” IEEE Control Systems Letters, vol. 2, no. 3, pp. 399–404, 2018.
    4. F. D. Brunner, M. A. Müller, and F. Allgöwer, “Enhancing Output-feedback MPC with Set-valued Moving Horizon Estimation,” IEEE Transactions on Automatic Control, vol. 63, no. 9, pp. 2976–2986, 2018.
    5. F. D. Brunner, D. Antunes, and F. Allgöwer, “Stochastic thresholds in event-triggered control: A consistent policy for quadratic control,” Automatica, vol. 89, pp. 376--381, 2018.
    6. L. Danish, D. Imig, F. Allgöwer, P. Scheurich, and N. Pollak, “Bcl-2-mediated control of TRAIL-induced apoptotic response in the non-small lung cancer cell line NCI-H460 is effective at late caspase processing steps,” PLoS One, vol. 13, no. 6, 2018.
    7. J. Feiling, A. Zeller, and C. Ebenbauer, “Derivative-Free Optimization Algorithms Based on Non-Commutative Maps,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 743–748, 2018.
    8. J. Feiling, S. Koga, M. Krstić, and T. R. Oliveira, “Gradient extremum seeking for static maps with actuation dynamics governed by diffusion PDEs,” Automatica, vol. 95, pp. 197--206, 2018.
    9. M. Hertneck, J. Köhler, S. Trimpe, and F. Allgöwer, “Learning an approximate model predictive controller with guarantees,” IEEE Control Systems Letters, vol. 2, no. 3, pp. 543--548, 2018.
    10. D. Imig, K. Kuritz, N. Pollak, M. Rehm, and F. Allgöwer, “Death patterns resulting from cell cycle-independent cell death,” IFAC-PapersOnLine, vol. 51, no. 19, pp. 90–93, 2018.
    11. A. Jensch et al., “The tumor suppressor protein DLC1 maintains protein kinase D activity and Golgi secretory function,” J.\ Biol.\ Chem., vol. 293, no. 37, pp. 14407–14416, 2018.
    12. K. Kuritz, S. Zeng, and F. Allgöwer, “Ensemble Controllability of Cellular Oscillators,” IEEE Control Systems Letters, vol. 3, no. 2, pp. 296–301, 2018.
    13. K. Kuritz, D. Imig, M. Dyck, and F. Allgöwer, “Ensemble control for cell cycle synchronization of heterogeneous cell populations,” IFAC-PapersOnLine, vol. 51, no. 19, pp. 44–47, 2018.
    14. J. Köhler, M. A. Müller, and F. Allgöwer, “Nonlinear reference tracking: An economic model predictive control  perspective,” IEEE Trans. Automat. Control, 2018.
    15. J. Köhler, M. A. Müller, and F. Allgöwer, “On periodic dissipativity notions in economic model predictive control,” IEEE Control Systems Letters, 2018.
    16. P. N. Köhler, M. A. Müller, and F. Allgöwer, “A distributed economic MPC framework for cooperative control under conflicting objectives,” Automatica, vol. 96, pp. 368–379, 2018.
    17. P. N. K�hler, M. A. M�ller, and F. Allg�wer, “A distributed economic MPC framework for cooperative control under conflicting objectives,” Automatica, vol. 96, pp. 368–379, 2018.
    18. F. A. Lincoln et al., “Sensitization of glioblastoma cells to TRAIL- induced apoptosis by IAP- and Bcl-2 antagonism,” Cell Death and Disease, vol. 9, no. 1112, 2018.
    19. S. Linsenmayer, H. Ishii, and F. Allgöwer, “Containability with event-based sampling for scalar systems with time-varying delay and uncertainty,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 725–730, 2018.
    20. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “Event-Based Vehicle Coordination Using Nonlinear Unidirectional Controllers,” IEEE Trans.\ Control of Network Systems, vol. 5, no. 4, pp. 1575–1584, 2018.
    21. D. Paul and N. Radde, “The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs,” Journal of Theoretical Biology, vol. 455, pp. 86–96, 2018.
    22. M. Gharbi and C. Ebenbauer, “A Proximity Approach to Linear Moving Horizon Estimation,” in The 6th IFAC NMPC Conference, Madison, USA., 2018, pp. 649–655.
    23. K. Kuritz, W. Halter, and F. Allgöwer, “Passivity-Based Ensemble Control for Cell Cycle Synchronization,” in Emerging Applications of Control and Systems Theory: A Festschrift in Honor of Mathukumalli Vidyasagar, R. Tempo, S. Yurkovich, and P. Misra, Eds. Cham: Springer International Publishing, 2018, pp. 1--13.
    24. W. Halter, F. Allgöwer, R. M. Murray, and A. Gyorgy, “Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts,” in Proc.\ 57th IEEE Conf.\ Decision and Control (CDC), Miami Beach, USA, 2018.
    25. S. Knüfer and M. A. Müller, “Robust Global Exponential Stability for Moving Horizon Estimation,” in Proc.\ 57th IEEE Conf.\ Decision and Control (CDC), 2018, pp. 3477–3482.
    26. J. Köhler, M. A. Müller, and F. Allgöwer, “MPC for nonlinear periodic tracking using reference generic offine computations,” in Proc.\ IFAC Conf.\ Nonlinear Model Predictive Control (NMPC), Madison, Wisconsin, 2018, pp. 656–661.
    27. J. Köhler, M. A. Müller, and F. Allgöwer, “A novel constraint tightening approach for nonlinear robust model  predictive control,” in Proc. American Control Conf. (ACC), 2018.
    28. J. Köhler, C. Enyioha, and F. Allgöwer, “Dynamic Resource Allocation to Control Epidemic Outbreaks -A Model Predictive Control Approach,” in Proc.\ American Control Conf.\ (ACC), Milwaukee, Wisconsin, 2018, pp. 1546–1551.
    29. J. Köhler, M. A. Müller, and F. Allgöwer, “Nonlinear Reference Tracking with Model Predictive Control: An Intuitive  Approach,” in Proc. European Control Conf. (ECC), 2018.
    30. P. N. Köhler, M. A. Müller, and F. Allgöwer, “Interconnections of dissipative systems and distributed economic MPC,” in Proc. 6th IFAC Conference on Nonlinear Model Predictive Control, Madison, Wisconsin, 2018, pp. 88–93.
    31. S. Linsenmayer and F. Allgöwer, “Performance oriented triggering mechanisms with guaranteed traffic characterization for linear discrete-time systems,” in Proc.\ European Control Conf.\ (ECC), Limassol, Cyprus, 2018, pp. 1474–1479.
    32. S. Michalowsky, B. Gharesifard, and C. Ebenbauer, “On the Lie bracket approximation approach to distributed optimization: Extensions and limitations,” in Proc.\European Control Conf.\ (ECC), Limassol, Cyprus, 2018, pp. 119–124.
    33. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Some ideas on sampling strategies for data-driven inference of passivity properties for MIMO systems,” in Proc. American Control Conference, 2018, pp. 6094--6100.
    34. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Data-driven inference of conic relations via saddle-point dynamics,” in Proc.\ 9th IFAC Symp.\ Robust Control Design, 2018, pp. 586--591.
    35. Wildhagen. S., S. Michalowsky, J. Feiling, and C. Ebenbauer, “Characterizing the learning dynamics in extremum seeking: The role of gradient averaging and non-convexity,” in Proc.\ 57th IEEE Conf.\ Decision and Control (CDC), Miami Beach, USA, 2018, pp. 21–26.
    36. S. Wildhagen, S. Michalowsky, J. Feiling, and C. Ebenbauer, “Characterizing the learning dynamics in extremum seeking: The role of gradient averaging and non-convexity,” in Proc. 57th IEEE Conf. Decision and Control (CDC), Miami Beach, USA, 2018, pp. 21–26.
    37. J. Berberich, J. Köhler, F. Allgöwer, and M. A. Müller, “Indefinite linear quadratic optimal control: periodic dissipativity and turnpike properties.” 2018.
    38. J. Berberich and F. Allgöwer, “A convex relaxation for learning linear dynamical systems with nonlinear sensors.” 2018.
    39. M. Gharbi and C. Ebenbauer, “Proximity-Moving-Horizon Sch�tzverfahren,” Proc.\ 56th IEEE Conf.\ Decision and Control (CDC). Melbourne, Victoria, Australia, pp. 2188–2194, 2018.
    40. W. Halter and F. Allgöwer, “Regelungstechnik in der Synthetischen Biologie: Konzeptionelle und experimentelle Realisierung von PID Reglern im Inneren von Zellen.” 2018.
    41. M. Hertneck, J. Köhler, S. Trimpe, and F. Allgöwer, “Learning a provably safe control law by merging model predictive control with machine learning.” 2018.
    42. K. Kuritz and F. Allgöwer, “Broadcast control of oscillating cell populations.” 2018.
    43. K. Kuritz and F. Allgöwer, “Therapy design by broadcast control of oscillating cell populations.” 2018.
    44. J. Köhler and F. Allgöwer, “Robust reference tracking with Model Predictive Control.” 2018.
    45. S. Linsenmayer and F. Allgöwer, “Networked Control Systems with advanced interfaces between control and communication.” 2018.
    46. S. Linsenmayer, “Bit rate bounds for containability of scalar systems with event-based  sampling.” 2018.
    47. S. Michalowsky and C. Ebenbauer, “Verteilte Optimierung und Regelung über gerichtete Graphen:  Ein neuer Ansatz mittels Lie Klammern.” 2018.
    48. C. E. Simon Michalowsky, “Verteilte Optimierung und Regelung über gerichtete Graphen:  Ein neuer Ansatz mittels Lie Klammern.” 2018.
    49. F. A. Wolfgang Halter, “Regelungstechnik in der Synthetischen Biologie: Konzeptionelle und  experimentelle Realisierung von PID Reglern im Inneren von Zellen.” 2018.
  3. 2017

    1. V. Avrutin, Zh. T. Zhusubaliyev, and E. Mosekilde, “Cascades of alternating pitchfork and flip bifurcations in H-bridge  inverters,” Physica~D, vol. 345, pp. 27–39, 2017.
    2. V. Avrutin, Zh. T. Zhusubaliyev, A. Saha, S. Banerjee, L. Gardini, and I. Sushko, “Dangerous Bifurcations Revisited,” Int. J. Bifurcat. Chaos, vol. 26, no. 14, p. 1630040, 2017.
    3. V. Avrutin, J. D. Morcillo, Zh. T. Zhusubaliyev, and F. Angulo, “Bubbling in a power electronic inverter: Onset, development and  detection,” Chaos, Solitons & Fractals, vol. 104, pp. 135–152, 2017.
    4. C. Feller and C. Ebenbauer, “A stabilizing iteration scheme for model predictive control based  on relaxed barrier functions,” Automatica, vol. 80, pp. 328--339, 2017.
    5. C. Feller and C. Ebenbauer, “Relaxed Logarithmic Barrier Function Based Model Predictive Control  of Linear Systems,” IEEE Trans. Automat. Control, vol. 62, no. 3, pp. 1223–1238, 2017.
    6. G. Goebel and F. Allgöwer, “New results on semi-explicit and almost explicit MPC algorithms,” at-Automatisierungstechnik, vol. 65, no. 4, pp. 245--259, 2017.
    7. G. Goebel and F. Allgöwer, “Semi-explicit MPC based on subspace clustering,” Automatica, vol. 83, pp. 309--316, 2017.
    8. W. Halter, J. M. Montenbruck, Z. A. Tuza, and F. Allgöwer, “A resource dependent protein synthesis model for evaluating synthetic  circuits,” J. Theor. Biol., vol. 420, pp. 267–278, 2017.
    9. B. Houska and M. A. Müller, “Cost-to-travel functions: a new perspective on optimal and model  predictive control,” Syst. Contr. Lett., vol. 106, pp. 79–86, 2017.
    10. A. Jensch, C. Thomaseth, and N. E. Radde, “Sampling-based Bayesian approaches reveal the importance of quasi-bistable  behavior in cellular decision processes on the example of the MAPK  signaling pathway in PC-12 cell lines,” BMC Sys. Biol., vol. 11, no. 1, p. 11, Jan. 2017.
    11. K. Kuritz, D. Stöhr, N. Pollak, and F. Allgöwer, “On the relationship between cell cycle analysis with ergodic principles  and age-structured cell population models,” J. Theor. Biol., vol. 414, pp. 91–102, 2017.
    12. Y. Liu et al., “Robust nonlinear control approach to nontrivial maneuvers and obstacle  avoidance for quadrotor UAV under disturbances,” Robotics and Autonomous Systems, vol. 98, pp. 317--332, 2017.
    13. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Stochastic MPC with Offline Uncertainty Sampling,” Automatica, vol. 81, pp. 176–183, 2017.
    14. M. Lorenzen, M. A. Müller, and F. Allgöwer, “Stochastic Model Predictive Control without Terminal Constraints,” Int. J. Robust and Nonlinear Control, 2017.
    15. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Stochastic MPC with Offline Uncertainty Sampling,” Automatica, vol. 81, pp. 176–183, 2017.
    16. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Constraint-Tightening and Stability in Stochastic Model Predictive  Control,” IEEE Trans. Automat. Control, vol. 62, no. 7, pp. 3165--3177, 2017.
    17. J. M. Montenbruck and S. Zeng, “Collinear Dynamical Systems,” Syst. Contr. Lett., vol. 105, pp. 34–43, 2017.
    18. J. M. Montenbruck, M. Arcak, and F. Allgöwer, “An Input-Output Framework for Submanifold Stabilization,” IEEE Trans. Automat. Control, vol. 62, no. 10, pp. 5170--5184, 2017.
    19. J. M. Montenbruck, D. Zelazo, and F. Allgöwer, “Fekete Points, Formation Control, and the Balancing Problem,” IEEE Trans. Automat. Control, vol. 62, no. 10, pp. 5069--5081, 2017.
    20. M. A. Müller and F. Allgöwer, “Economic and distributed model predictive control: recent developments  in optimization-based control,” SICE Journal of Control, Measurement, and System Integration, vol. 10, no. 2, pp. 39–52, 2017.
    21. M. A. Müller and K. Worthmann, “Quadratic costs do not always work in MPC,” Automatica, vol. 82, pp. 269–277, 2017.
    22. M. A. Müller, “Nonlinear moving horizon estimation in the presence of bounded disturbances,” Automatica, vol. 79, pp. 306–314, 2017.
    23. A. Panchuk, I. Sushko, and V. Avrutin, “Bifurcation Structures in a Bimodal Piecewise Linear Map,” Front. Appl. Math. Stat., vol. 3, no. 7, pp. 1–21, 2017.
    24. C. Thomaseth, K. Kuritz, F. Allgoewer, and R. N., “The circuit-breaking algorithm for monotone systems,” Mathematical Biosciences, vol. 284, pp. 80–91, 2017.
    25. S. Zeng and F. Allgöwer, “Structured optimal feedback in multi-agent systems: A static output  feedback perspective,” Automatica, vol. 76, pp. 214–221, 2017.
    26. A. Zuyev and V. Grushkovskaya, “Motion planning for control-affine systems satisfying low-order controllability  conditions,” Int. J. Control, vol. 90, no. 11, pp. 2517--2537, 2017.
    27. A. P. Aguiar et al., “Constrained Optimal Motion Planning for Autonomous Vehicles Using  PRONTO,” in Sensing and Control for Autonomous Vehicles: Applications to Land,  Water and Air Vehicles, T. I. Fossen, K. Y. Pettersen, and H. Nijmeijer, Eds. Springer International Publishing, 2017, pp. 207--226.
    28. V. Avrutin, \zh.T. Zhusubaliyev, and E. Mosekilde, “Low-dimensional piecewise smooth maps with an unpredictable number  of switching manifolds,” in Proc. 9th European Nonlinear Dynamics Conference (ENOC), Budapest, Hungary, 2017.
    29. B. W. Carabelli, R. Blind, F. Dürr, and K. Rothermel, “State-dependent priority scheduling for networked control systems,” in Proc. American Control Conf. (ACC), 2017.
    30. C. Ebenbauer, S. Michalowsky, V. Grushkovskaya, and B. Gharesifard, “Distributed Optimization over Directed Graphs with the help of Lie  Brackets,” in Proc. 20th IFAC World Congress, Toulouse, France, 2017, vol. 50, no. 1, pp. 15343--15348.
    31. M. Gharbi, C. Feller, and C. Ebenbauer, “A first step toward moving horizon state estimation based on relaxed  logarithmic barrier functions,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, 2017, pp. 2188–2194.
    32. V. Grushkovskaya, H.-B. Dülrr, C. Ebenbauer, and A. Zuyev, “Extremum Seeking for Time-Varying Functions using Lie Bracket Approximations,” in Proc. 20th IFAC World Congress, Toulouse, France, 2017, vol. 50, no. 1, pp. 5522--5528.
    33. W. Halter, Z. A. Tuza, and F. Allgöwer, “Signal differentiation with genetic networks,” in Proc. 20th IFAC World Congress, Toulouse, France, 2017.
    34. W. Halter, J. M. Montenbruck, and F. Allgöwer, “Systems with integral resource consumption,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Australia, 2017.
    35. J. Köhler, M. A. Müller, N. Li, and F. Allgöwer, “Real Time Economic Dispatch for power networks: A Distributed Economic  Model Predictive Control Approach,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, 2017, pp. 6340–6345.
    36. P. N. Köhler, M. A. Müller, and F. Allgöwer, “Transient performance of economic model predictive control with average  constraints,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, 2017, pp. 5557–5562.
    37. P. N. Köhler, M. A. Müller, J. Pannek, and F. Allgöwer, “On Exploitation of Supply Chain Properties by Sequential Distributed  MPC.,” in Proc. of the 20th IFAC World Congress, Toulouse, France, 2017, pp. 8219–8224.
    38. S. Linsenmayer, R. Blind, and F. Allgöwer, “Delay-dependent data rate bounds for containability of scalar systems,” in Proc. of the 20th IFAC World Congress, Toulouse, France, 2017, pp. 7875–7880.
    39. S. Linsenmayer and F. Allgöwer, “Stabilization of Networked Control Systems with weakly hard real-time  dropout description,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Australia, 2017, pp. 4765–4770.
    40. M. Lorenzen, F. Allgöwer, and M. Cannon, “Adaptive Model Predictive Control with Robust Constraint Satisfaction,” in Proc. of the 20th IFAC World Congress, Toulouse, France, 2017, pp. 3368--3373.
    41. M. Lorenzen, M. A. Müller, and F. Allgöwer, “Stabilizing Stochastic MPC without Terminal Constraints,” in Proc. American Control Conf. (ACC), Seattle, Washington, 2017, pp. 5636--5641.
    42. S. Michalowsky, B. Gharesifard, and C. Ebenbauer, “Distributed extremum seeking over directed graphs,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, 2017, pp. 2095–2101.
    43. J. M. Montenbruck, S. Zeng, and F. Allgöwer, “Linear Systems with Quadratic Outputs,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2017, pp. 1030–1034.
    44. J. M. Montenbruck and F. Allgöwer, “Separable matrices and minimum complexity controllers,” in Proc. 56th IEEE Conf. Decision and Control (CDC), 2017, pp. 4187--4192.
    45. M. A. Müller and L. Grüne, “On the relation between dissipativity and discounted dissipativity,” in Proc. 56th IEEE Conf. Decision and Control (CDC), 2017, pp. 5570–5575.
    46. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Sampling strategies for data-driven inference of passivity properties,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Victoria, Australia, 2017, pp. 6389--6394.
    47. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Determining dissipation inequalities from input-output samples,” in Proc. 20th IFAC World Congress, 2017, pp. 7789--7794.
    48. S. Zeng, J. M. Montenbruck, and F. Allgöwer, “Periodic Signal Compressors,” in Proc. 20th World Congress of the International Federation of Automatic  Control, 2017, pp. 6649--6654.
    49. A. Zuyev and V. Grushkovskaya, “Obstacle Avoidance Problem for Driftless Nonlinear Systems with Oscillating  Controls,” in Proc. 20th IFAC World Congress, Toulouse, France, 2017, vol. 50, no. 1, pp. 10476--10481.
    50. D. Imig, N. Pollak, and F. Allgöwer, “Cell population dynamics during apoptotic treatment.” 2017.
    51. P. N. Köhler, M. A. Müller, and F. Allgöwer, “Distributed Model Predictive Control -- Coordination of interconnected  systems.” 2017.
    52. S. Linsenmayer and F. Allgöwer, “Event-based sampling concepts for Networked Control Systems.” 2017.
    53. S. Linsenmayer and F. Allgöwer, “On the potential of event-based sampling for control with limited  data rate.” 2017.
    54. S. Linsenmayer and F. Allgöwer, “Stability analysis and control design for weakly hard real-time systems.” 2017.
    55. S. Michalowsky and C. Ebenbauer, “Distributed optimization and extremum seeking over directed graphs.” 2017.
    56. C. E. Simon Michalowsky, “Distributed optimization and extremum seeking over directed graphs.” 2017.
  4. 2016

    1. V. Avrutin, Zh. T. Zhusubaliyev, A. El Aroudi, D. Fournier-Prunaret, G. Garcia, and E. Mosekilde, “Disrupted bandcount doubling in an AC-DC boost PFC circuit  modeled by a time varying map,” J. of Physics, vol. 692, no. 1, p. 012003, 2016.
    2. V. Avrutin, Zh. T. Zhusubaliyev, and E. Mosekilde, “Border collisions inside the stability domain of a fixed point,” Physica~D, vol. 321–322, pp. 1–15, 2016.
    3. F. A. Bayer, M. Lorenzen, M. A. Müller, and F. Allgöwer, “Robust Economic Model Predictive Control using Stochastic Information,” Automatica, vol. 74, pp. 151–161, 2016.
    4. M. Belopolskaya, V. Avrutin, S. Firsov, and A. Yakovlev, “Potential Applications of Serum HBsAg Level Measurement in  Patients with Hepatitis B and D Co-Infection,” Gastroenterology & Hepatology, vol. 5, no. 7, p. 00170, 2016.
    5. F. D. Brunner, M. Heemels, and F. Allgöwer, “Robust self-triggered MPC for constrained linear systems: A tube-based  approach,” Automatica, vol. 72, pp. 73--83, 2016.
    6. V. Grushkovskaya, “On the influence of resonances on the asymptotic behavior of trajectories  of nonlinear systems in critical cases,” Nonlinear Dynamics, vol. 86, no. 1, pp. 587--603, 2016.
    7. V. Grushkovskaya, “Asymptotic behavior of solutions of nonlinear systems with multiple  imaginary eigenvalues,” PAMM, vol. 16, no. 1, pp. 271--272, 2016.
    8. L. Grüne and M. A. Müller, “On the relation between strict dissipativity and turnpike properties,” System & Control Letters, vol. 90, pp. 45–53, 2016.
    9. J. Kirch, C. Thomaseth, A. Jensch, and N. Radde, “The effect of model rescaling and normalization on sensitivity analysis  on an example of a MAPK pathway model,” Eur. Phys. J. Nonlin. Biomed. Phys., vol. 4, no. 3, 2016.
    10. K. D. Listmann, P. Wenzelburger, and F. Allgöwer, “Industrie 4.0 - (R)evolution ohne Regelungstechnik?,” at-Automatisierungstechnik, vol. 64, no. 7, pp. 507–520, 2016.
    11. K. D. Listmann, P. Wenzelburger, and F. Allgöwer, “Industrie 4.0 - (R)evolution without Control Technologies?,” J. of The Society of Instrument and Control Engineers, vol. 55, no. 7, pp. 555–565, 2016.
    12. J. M. Montenbruck and F. Allgöwer, “Asymptotic Stabilization of Submanifolds Embedded in Riemannian  Manifolds,” Automatica, vol. 74, pp. 349--359, 2016.
    13. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Compensating Drift Vector Fields with Gradient Vector Fields for  Asymptotic Submanifold Stabilization,” IEEE Trans. Automat. Control, vol. 61, no. 2, pp. 388–399, 2016.
    14. M. A. Müller and L. Grüne, “Economic model predictive control without terminal constraints for  optimal periodic behavior,” Automatica, vol. 70, pp. 128–139, 2016.
    15. N. Radde and M.-T. Hütt, “The Physics behind Systems Biology,” Eur. Phys. J. Nonlin. Biomed. Phys., vol. 4, no. 1, 2016.
    16. D. Schittler, T. Jouini, F. Allgöwer, and S. Waldherr, “Multistability equivalence between gene regulatory networks of different  dimensionality with application to a differentiation network,” Int. J. Robust and Nonlinear Control, 2016.
    17. G. S. Seyboth, W. Ren, and F. Allgöwer, “Cooperative control of linear multi-agent systems via distributed output regulation and transient synchronization,” Automatica, vol. 68, pp. 132–139, 2016.
    18. I. Sushko, L. Gardini, and V. Avrutin, “Nonsmooth One-dimensional Maps: Some Basic Concepts and Definitions,” J. Differ. Equations Appl., vol. 22, no. 12, pp. 1816–1870, 2016.
    19. J. Wu and F. Allgöwer, “Verteilte Zustandsschätzung zur Ausgangsregulierung von verteilten  Systemen mit gekoppelten Messgrößen,” at-Automatisierungstechnik, vol. 64, no. 8, pp. 645–657, 2016.
    20. S. Zeng, S. Waldherr, C. Ebenbauer, and F. Allgöwer, “Ensemble Observability of Linear Systems,” IEEE Trans. Automat. Control, vol. 61, no. 6, pp. 1452–1465, 2016.
    21. S. Zeng and F. Allgøwer, “A moment-based approach to ensemble controllability of linear systems,” Syst. Contr. Lett., vol. 98, pp. 49–56, 2016.
    22. B. Ács, G. Szederkényi, Zs. Tuza, and Z. A. Tuza, “Computing all possible graph structures describing linearly conjugate  realizations of kinetic systems,” Computer Physics Communications, vol. 204, pp. 11–20, 2016.
    23. D. Paul, L. Dehkordi Fayegh Koohi, M. von Scheven, and M. Bischoff, “Biologically Design and Integrative Structures - Analysis, Simulation,  Implementation in Architecture,” J. Knippers, K. Nickel, and T. Speck, Eds. Springer International Publishing AG, 2016.
    24. E. Aydiner, M. A. Müller, and F. Allgöwer, “Periodic Reference Tracking for Nonlinear Systems via Model Predictive  Control,” in Proc. European Control Conf. (ECC), Aalborg, Denmark, 2016, pp. 2602--2607.
    25. F. A. Bayer, F. D. Brunner, M. Lazar, M. G. A. Wijnand, and F. Allgöwer, “A Tube-Based Approach to Nonlinear Explicit MPC,” in Proc. 55th IEEE Conf. Decision and Control (CDC), 2016, pp. 4059--4064.
    26. F. A. Bayer, M. A. Müller, and F. Allgöwer, “Min-max Economic Model Predictive Control Approaches with Guaranteed  Performance,” in Proc. 55th IEEE Conf. Decision and Control (CDC), 2016, pp. 3210--3215.
    27. F. D. Brunner and F. Allgöwer, “A Lyapunov Function Approach to the Event-triggered Stabilization  of the Minimal Robust Positively Invariant Set,” in Proc. 6th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Tokyo, Japan, 2016, vol. 49, no. 22, pp. 25--30.
    28. F. D. Brunner, W. P. . M. H. Heemels, and F. Allgöwer, “Numerical Evaluation of a Robust Self-Triggered MPC Algorithm,” in Proc. 6th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Tokyo, Japan, 2016, vol. 49, no. 22, pp. 151--156.
    29. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Dynamic Thresholds in Robust Event-Triggered Control for Discrete-Time  Linear Systems,” in Proc. European Control Conf. (ECC), Aalborg, Denmark, 2016, pp. 983--988.
    30. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “$\gamma$-Invasive Event-triggered and Self-triggered Control for  Perturbed Linear Systems,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 1346–1351.
    31. F. D. Brunner, F. A. Bayer, and F. Allgöwer, “Robust Steady State Optimization for Polytopic Systems,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 4084–4089.
    32. F. D. Brunner, M. A. Müller, and F. Allgöwer, “Enhancing Output Feedback MPC for Linear Discrete-time Systems  with Set-valued Moving Horizon Estimation,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 2733–2738.
    33. C. Feller and C. Ebenbauer, “Robust stability properties of MPC iteration schemes based on relaxed  barrier functions,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 1484–1489.
    34. C. Feller, M. Ouerghi, and C. Ebenbauer, “Robust output feedback model predictive control based on relaxed  barrier functions,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 1477–1483.
    35. V. Grushkovskaya and C. Ebenbauer, “Multi-Agent Coordination with Lagrangian Measurements,” in Proc. 6th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Tokyo, Japan, 2016, pp. 115–120.
    36. W. Halter, J. M. Montenbruck, and F. Allgöwer, “Geometric stability considerations of the ribosome flow model with  pool,” in Proc. 22nd Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Minneapolis, MN, USA, 2016, pp. 424–429.
    37. J. Kim, H. Shim, and J. Wu, “Distributed optimal kalman-bucy filter with guaranteed stability,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, 2016.
    38. S. Knüfer, M. A. Müller, and F. Allgöwer, “Stabilizing Model Predictive Control without Terminal Constraints  for Switched Nonlinear Systems,” in Proc. 10th IFAC Symp. Nonlinear Control Systems (NOLCOS), Monterey, CA, USA, 2016, pp. 65–70.
    39. P. N. Köhler, M. A. Müller, and F. Allgöwer, “A distributed economic MPC scheme for coordination of self-interested  systems,” in Proc. American Control Conf. (ACC), Boston, MA, USA, 2016, pp. 889--894.
    40. P. N. Köhler and D. V. Dimarogonas, “On topological conditions to maintain leader-follower connectivity  in double-integrator multi-agent systems,” in Proc. 24th Mediterranean Conf. Control and Automation (MED), Athens, Greece, 2016, pp. 767--772.
    41. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “A non-monotonic approach to periodic event-triggered control with  packet loss,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 507–512.
    42. S. Michalowsky and C. Ebenbauer, “Gradient approximation and extremum seeking via needle variations,” in Proc. American Control Conf. (ACC), Boston, MA, USA, 2016, pp. 6091–6096.
    43. S. Michalowsky and C. Ebenbauer, “Extremum control of linear systems based on output feedback,” in Proc.55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 2963–2968.
    44. J. M. Montenbruck and F. Allgöwer, “Input-Output Control of Composite Systems,” in Proc. 55th IEEE Conf. Decision and Control (CDC), 2016.
    45. J. M. Montenbruck and F. Allgöwer, “Some Problems Arising in Controller Design from Big Data via Input-Output  Methods,” in Proc. 55th IEEE Conf. Decision and Control (CDC), 2016.
    46. J. M. Montenbruck, S. Zeng, and F. Allgöwer, “On the Observability Properties of Systems with Rolling Shutter,” in Proc. 54th Annual Allerton Conf. on Communication, Control, and Computing, 2016.
    47. J. M. Montenbruck and F. Allgöwer, “Persistence of Excitation and the Feedback Theorem for Passive Systems,” in Proc. 10th IFAC Symp. Nonlinear Control Systems (NOLCOS), 2016.
    48. M. A. Müller, “Nonlinear moving horizon estimation for systems with bounded disturbances,” in Proc. American Control Conf. (ACC), 2016, pp. 883–888.
    49. S. K. Niederländer, A. F., and C. J., “Exponentially Fast Distributed Coordination for Nonsmooth Convex  Optimization,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 1036–1041.
    50. I. Notarnicola, F. A. Bayer, G. Notarstefano, and F. Allgöwer, “Final-State Constrained Optimal Control via a Projection Operator  Approach,” in Proc. European Control Conf. (ECC), 2016, pp. 148–153.
    51. D. Paul and N. Radde, “Robustness and filtering properties of ubiquitous signaling network  motifs,” in Proc. 6th Foundations of Systems Biology in Engineering (FOSBE), Magdeburg, Germany, 2016.
    52. Z. A. Tuza, B. Ács, G. Szederkényi, and F. Allgöwer, “Efficient Computation of All Distinct Realization Structures of Kinetic  Systems,” in IFAC-PapersOnLine, 2016, vol. 49, no. 26, pp. 194--200.
    53. J. Wu, A. Elser, S. Zeng, and F. Allgöwer, “Consensus-based distributed Kalman-Bucy filter for continuous-time  systems,” in Proc. 6th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), 2016.
    54. J. Wu, V. Ugrinovskii, and F. Allgöwer, “Observer-based synchronization with relative measurements and unknown  neighbour models,” in Proc. Australian Control Conf. (AuCC), Newcastle, Australia, 2016.
    55. S. Zeng and F. Allgöwer, “A General Sampled Observability Result and Its Applications,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 3997--4002.
    56. S. Zeng and F. Allgöwer, “On the Ensemble Observability of Dynamical Systems,” in Proc. 22nd Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Minnesota, Minneapolis, USA, 2016, pp. 685--688.
    57. S. Zeng and F. Allgöwer, “On the Moment Dynamics of Discrete Measures,” in Proc. 55th IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2016, pp. 4901--4906.
    58. S. Zeng, H. Ishii, and F. Allgöwer, “State estimation of interconnected ensembles with anonymized outputs,” in Proc. 6th IFAC Workshop on Distributed Estimation and Control in  Networked Systems, Tokyo, Japan, 2016.
    59. A. Zuyev, V. Grushkovskaya, and P. Benner, “Time-varying stabilization of a class of driftless systems satisfying  second-order controllability conditions,” in Proc. European Control Conf. (ECC), Aalbrog, Denamrk, 2016, pp. 575–580.
    60. K. Kuritz, F. Müller, N. Pollak, and F. Allgöwer, “Too Young to Die: Age Structured Population Models Capture Cell Cycle  Dependent Apoptosis from Snapshot Data.” 2016.
    61. S. Linsenmayer and F. Allgöwer, “Event-based methods in Net-CPS: An example and arising challenges.” 2016.
    62. C. Thomaseth and N. Radde, “Normalization of Western blot data affects the statistics of estimators.” 2016.
    63. C. Breindl, “Identification, analysis and control of discrete and continuous models  of gene regulation networks,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2016.
    64. G. S. Seyboth, “On Distributed and Cooperative Control Design for Networks of Dynamical  Systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2016.
  5. 2015

    1. V. Avrutin, B. Schenke, and L. Gardini, “Calculation of homoclinic and heteroclinic orbits in 1D maps,” Communications in Nonlinear Science and Numerical Simulation, vol. 22, no. 1–3, pp. 1201–1214, 2015.
    2. V. Avrutin, E. Mosekilde, Zh. T. Zhusubaliyev, and L. Gardini, “Onset of chaos in a single-phase power electronic inverter,” Chaos, vol. 25, p. 043114, 2015.
    3. V. Avrutin, M. Clüver, V. Mahout, and D. Fournier-Prunaret, “Bandcount adding structure and collapse of chaotic attractors in  a piecewise linear bimodal map,” Physica D, vol. 309, pp. 37–56, 2015.
    4. V. Avrutin, Ch. Dibak, A. Dal Forno, and U. Merlone, “Dynamics of a 2D piecewise linear Braess paradox model: Effect  of the third partition,” Int. J. Bifurcat. Chaos, vol. 25, no. 11, p. 1530031, 2015.
    5. M. Belopolskaya, V. Avrutin, S. Firsov, and A. Yakovlev, “HBsAg level and Hepatitis B viral load correlation with  focus on pregnancy,” Annals of Gastroenterology, vol. 28, no. 3, pp. 1–6, 2015.
    6. F. D. Brunner, M. Lazar, and F. Allgöwer, “Stabilizing model predictive control: On the enlargement of the terminal  set,” Int. J. Robust and Nonlinear Control, vol. 25, no. 15, pp. 2646–2670, 2015.
    7. M. Bürger and C. De Persis, “Dynamic Coupling Design for Nonlinear Output Agreement and Time-varying  Flow Control,” Automatica, vol. 51, pp. 210–222, 2015.
    8. D. Imig, N. Pollak, T. Strecker, P. Scheurich, F. Allgöwer, and S. Waldherr, “An individual-based simulation framework for dynamic, heterogeneous  cell populations during extrinsic stimulations,” J. Coupled Syst. Multiscale Dyn., vol. 3, no. 2, pp. 143–155, 2015.
    9. J. M. Montenbruck, G. S. Schmidt, G. S. Seyboth, and F. Allgöwer, “On the Necessity of Diffusive Couplings in Linear Synchronization  Problems with Quadratic Cost,” IEEE Trans. Automat. Control, vol. 60, no. 11, pp. 3029–3034, 2015.
    10. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Practical Synchronization with Diffusive Couplings,” Automatica, vol. 53, pp. 235–243, 2015.
    11. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Synchronization of Diffusively Coupled Systems on Compact Riemannian  Manifolds in the Presence of Drift,” Syst. Contr. Lett., vol. 76, pp. 19–27, 2015.
    12. M. A. Müller, D. Liberzon, and F. Allgöwer, “Norm-controllability of nonlinear systems,” IEEE Trans. Automat. Control, vol. 60, no. 7, pp. 1825–1840, 2015.
    13. M. A. Müller, D. Angeli, and F. Allgöwer, “On necessity and robustness of dissipativity in economic model predictive  control,” IEEE Trans. Automat. Control, vol. 60, no. 6, pp. 1671–1676, 2015.
    14. A. Panchuk, I. Sushko, and V. Avrutin, “Bifurcation structures in a bimodal piecewise linear map: chaotic  dynamics,” Int. J. Bifurcat. Chaos, vol. 25, no. 3, 2015.
    15. G. Seyboth, D. V. Dimarogonas, K. H. Johansson, P. Frasca, and F. Allgöwer, “On Robust Synchronization of Heterogeneous Linear Multi-Agent Systems  with Static Couplings,” Automatica, vol. 53, pp. 392–399, 2015.
    16. I. Sushko, V. Avrutin, and L. Gardini, “Bifurcation structure in the skew tent map and its application as  a border collision normal form,” J. of Diff. Equations and Applications, pp. 1–48, 2015.
    17. I. Sushko, F. Tramontana, F. Westerhoff, and V. Avrutin, “Symmetry breaking in a bull and bear financial market model,” Chaos, Solinons and Fractals, vol. 79, pp. 57–72, 2015.
    18. F. Tramontana, I. Sushko, and V. Avrutin, “Period adding structure in a 2D discontinuous model of economic growth,” Applied Mathematics and Computation, vol. 253, pp. 262–273, 2015.
    19. P. Weber, M. Hornjik, M. A. Olayioye, A. Hausser, and N. Radde, “A computational model of PKD and CERT interactions at the trans-Golgi  network of mammalian cells,” BMC Sys. Biol., vol. 9, no. 1, p. 147, 2015.
    20. E. Aydiner, F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Robust Self-Triggered Model Predictive Control for Constrained Discrete-Time  LTI Systems based on Homothetic Tubes,” in Proc. European Control Conf. (ECC), Linz, Austria, 2015, pp. 1587–1593.
    21. F. A. Bayer, M. A. Müller, and F. Allgöwer, “Average Constraints in Robust Economic Model Predictive Control,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), Whistler, Britisch Columbia, Canada, 2015, pp. 44–49.
    22. F. A. Bayer, M. Lorenzen, M. A. Müller, and F. Allgöwer, “Improving Performance in Robust Economic MPC Using Stochastic Information,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Seville, Spain, 2015, vol. 48, no. 23, pp. 410–415.
    23. R. Blind and F. Allgöwer, “Towards Networked Control Systems with Guaranteed Stability: Using  Weakly Hard Real-Time Constraints to Model the Loss Process,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 7510–7515.
    24. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Robust Event-Triggered MPC for Constrained Linear Discrete-Time  Systems with Guaranteed Average Sampling Rate,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Seville, Spain, 2015, vol. 48, no. 23, pp. 117–122.
    25. F. D. Brunner, T. M. P. Gommans, W. P. M. H. Heemels, and F. Allgöwer, “Resource-aware set-valued estimation for discrete-time linear systems,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 5480–5486.
    26. F. D. Brunner, T. M. P. Gommans, W. P. M. H. Heemels, and F. Allgöwer, “Communication Scheduling in Robust Self-Triggered MPC for Linear  Discrete-Time Systems,” in Proc. 5th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Philadelphia, PA, USA, 2015, vol. 48, no. 22, pp. 132–137.
    27. C. Feller and C. Ebenbauer, “Weight recentered barrier functions and smooth polytopic terminal  set formulations for linear model predictive control,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 1647–1652.
    28. C. Feller and C. Ebenbauer, “Input-to-state stability properties of relaxed barrier function based  MPC,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Seville, Spain, 2015, vol. 48, no. 23, pp. 302–307.
    29. G. Goebel and F. Allgöwer, “A Simple Semi-Explicit MPC Algorithm,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Seville, Spain, 2015, vol. 48, no. 23, pp. 489–494.
    30. W. Halter, N. Kress, K. Otte, S. Reich, B. Hauer, and F. Allgöwer, “Yield-Analysis of Different Coupling Schemes for Interconnected Bio-Reactors,” in Proc. SIAM Conf. Control and its Applications, Paris, France, 2015, pp. 384–391.
    31. S. Linsenmayer and D. V. Dimarogonas, “Event-triggered Control for Vehicle Platooning,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 3101–3106.
    32. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “Nonlinear Event-Triggered Platooning Control with Exponential Convergence,” in Proc. 5th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Philadelphia, PA, USA, 2015, vol. 48, no. 22, pp. 138–143.
    33. M. Lorenzen, F. Allgöwer, F. Dabbene, and R. Tempo, “An Improved Constraint-Tightening Approach for Stochastic MPC,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 944–949.
    34. M. Lorenzen, F. Allgöwer, F. Dabbene, and R. Tempo, “Scenario-Based Stochastic MPC with Guaranteed Recursive Feasibility,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 4958–4963.
    35. S. Michalowsky and C. Ebenbauer, “Model-based extremum seeking for a class of nonlinear systems,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 2026–2031.
    36. J. M. Montenbruck, H.-B. Dürr, C. Ebenbauer, and F. Allgöwer, “Extremum Seeking with Drift,” in Proc. 1st MICNON, St. Petersburg, Russia, 2015, vol. 48, no. 11, pp. 126–130.
    37. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Navigation and Obstacle Avoidance via Backstepping for Mechanical  Systems with Drift in the Closed Loop,” in Proc. 2015 American Control Conference, Chicago, IL, USA, 2015, pp. 625–630.
    38. J. M. Montenbruck, D. Zelazo, and F. Allgöwer, “Retraction Balancing and Formation Control,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 3645–3650.
    39. J. M. Montenbruck, A. Birk, and F. Allgöwer, “A Convex Conic Underestimate of Laplacian Spectra and its Application  to Network Synthesis,” in Proc. European Control Conf. (ECC), Linz, Austria, 2015, pp. 563–568.
    40. J. M. Montenbruck, M. Arcak, and F. Allgöwer, “Stabilizing Submanifolds with Passive Input-Output Relations,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 4381–4387.
    41. J. M. Montenbruck, G. S. Schmidt, A. Kecskeméthy, and F. Allgöwer, “Two Gradient-Based Control Laws on SE(3) Derived from Distance  Functions,” in Interdisciplinary Applications of Kinematic, 2015, vol. 2, pp. 31–41.
    42. M. A. Müller, L. Grüne, and F. Allgöwer, “On the role of dissipativity in economic model predictive control,” in Proc. 5th IFAC Conf. Nonlinear Model Predictive Control (NMPC), 2015, vol. 48, no. 23, pp. 110–116.
    43. M. A. Müller and L. Grüne, “Economic model predictive control without terminal constraints: optimal  periodic operation,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 4946–4951.
    44. S. K. Niederländer and J. Cortés, “Distributed Coordination for Separable Convex Optimization with Coupling  Constraints,” in Proc. 54th IEEE Conf. Decision and Control (CDC), Osaka, Japan, 2015, pp. 694–699.
    45. N. Radde and S. Klaus, “Bifurcation analysis for intracellular regulation networks based  on their circuit structure,” in Proc. 9th IFAC Symp. Biological and Medical Systems, Berlin, Germany, 2015, vol. 48, no. 20, pp. 165–170.
    46. G. Seyboth and F. Allgöwer, “Output Synchronization of Linear Multi-Agent Systems under Constant  Disturbances via Distributed Integral Action,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 62–67.
    47. Z. Sun, G. Seyboth, and B. D. O. Andersion, “Collective control of multiple unicycle agents with non-identical  constant speeds: Tracking control and performance limitation,” in Proc. IEEE Conf. Control Applications (CCA), Part of IEEE Multi-Conference  on Systems and Control (MSC), Sydney, Australia, 2015, pp. 1361–1366.
    48. J. Wu, L. Li, V. Ugrinovskii, and F. Allgöwer, “Distributed filter design for cooperative $H_ınfty$-type estimation,” in Proc. IEEE Multiconf. Systems and Control (MSC), Sydney, Australia, 2015, pp. 1373–1378.
    49. J. Wu, V. Ugrinovskii, and F. Allgöwer, “Cooperative $H_ınfty$ estimation for large-scale interconnected  linear systems,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 2119–2124.
    50. S. Zeng, H. Ishii, and F. Allgöwer, “Sampled Observability of Discrete Heterogeneous Ensembles from Anonymized  Output Measurements,” in Proc. 54th IEEE Conf. Decision and Control (CDC), 2015, pp. 5683–5688.
    51. S. Zeng, H. Ishii, and F. Allgöwer, “On the state estimation problem for discrete ensembles from discrete-time  output snapshots,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2015, pp. 4844–4849.
    52. S. Zeng and F. Allgöwer, “On the ensemble observability problem for nonlinear systems,” in Proc. 54th IEEE Conf. Decision and Control (CDC), 2015, pp. 6318–6323.
    53. A. Jensch, C. Thomaseth, and N. Radde, “Sampling-Based Analysis of Feedback in a Model of the MAP Kinase  Pathway.” 2015.
    54. K. Kuritz and F. Allgöwer, “Inferring cell-cycle dependent signalling with age-structured population  models.” 2015.
    55. C. Thomaseth, A. Jensch, and N. @ARTICLEist:thomaseth17a Radde author =. Thomaseth, C. and Kuritz, K. and Allgoewer, F. and Radde N. ,. title =. The circuit-breaking algorithm for monotone systems, journal =. Mathematical Biosciences, year =. 2017, volume =. 284, pages =. 80-91 @ARTICLEist:thomaseth17a, author =. Thomaseth, C. and Kuritz, K. and Allgoewer, F. and Radde N. ,. title =. The circuit-breaking algorithm for monotone systems, journal =. Mathematical Biosciences, year =. 2017, volume =. 284, pages =. 80-91, “Positive and negative feedback-endowed pathways show robustness against  biological heterogeneity and stochastic fluctuations.” 2015.
    56. C. Thomaseth and N. Radde, “Model calibration: the statistical effects of data normalization.” 2015.
    57. C. Thomaseth, A. Jensch, and N. Radde, “Positive and negative feedback-endowed pathways show robustness against biological heterogeneity and stochastic fluctuations.” 2015.
    58. H. B. Dürr, “Constrained Extremum Seeking: A Lie bracket and Singular Perturbation  Approach,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2015.
    59. A. Kramer, “Stochastic Methods for Parameter Estimation and Design of Experiments  in Systems Biology,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2015.
    60. D. Schittler, “A mathematical modeling framework to simulate and analyze cell type  transitions,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2015.
    61. S. Schuler, “Controller and Network Design Exploiting System Structure,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2015.
    62. P. M. Weber, “Data-driven modeling of molecular interactions at the trans-Golgi  network of mammalian cells,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2015.
  6. 2014

    1. V. Avrutin, L. Gardini, M. Schanz, and I. Sushko, “Bifurcations of chaotic attractors in one-dimensional piecewise smooth  maps,” Int. J. Bifurcat. Chaos, vol. 24, no. 8, p. 1440012, 2014.
    2. V. Avrutin, I. Sushko, and F. Tramontana, “Bifurcation structure in a bimodal piecewise linear business cycle  model,” Abstract and Applied Analysis, vol. 2014, p. Article ID 401319, 12 pages, 2014.
    3. V. Avrutin, B. Eckstein, M. Schanz, and B. Schenke, “Bandcount incrementing scenario revisited and floating regions within  robust chaos,” Mathematics and Computers in Simulation (Special Issue ``Discontinuous  Dynamical Systems: Theory and Numerical Methods’’), vol. 95, pp. 23–38, 2014.
    4. V. Avrutin, I. Sushko, and L. Gardini, “Cyclicity of chaotic attractors in one-dimensional discontinuous  maps,” Mathematics and Computers in Simulation (Special Issue ``Discontinuous  Dynamical Systems: Theory and Numerical Methods’’), vol. 95, pp. 126–136, 2014.
    5. V. Avrutin, I. Sushko, and L. Gardini, “Cyclicity of chaotic attractors in one-dimensional discontinuous maps,” Mathematics and Computers in Simulation (Special Issue ``Discontinuous Dynamical Systems: Theory and Numerical Methods’’), vol. 95, pp. 126–136, 2014.
    6. F. Bayer, M. A. Müller, and F. Allgöwer, “Tube-based Robust Economic Model Predictive Control,” J. Proc. Contr., vol. 24, no. 8, pp. 1237–1246, 2014.
    7. M. Bürger, C. De Persis, and F. Allgöwer, “Dynamic Pricing Control for Constrained Distribution Networks with  Storage,” IEEE Trans. Control of Network Systems, vol. 2, no. 1, pp. 88–97, 2014.
    8. M. Bürger, G. Notarstefano, and F. Allgöwer, “A Polyhedral Approximation Framework for Convex and Robust Distributed  Optimization,” IEEE Transactions on Automatic Control, vol. 59, no. 2, pp. 384–395, 2014.
    9. A. Dal Forno, U. Merlone, and V. Avrutin, “Dynamics in Braess paradox with non-impulsive commuters,” Discrete Dynamics in Nature and Society, vol. 2014, p. Article ID 345795, 13 pages, 2014.
    10. L. Gardini, V. Avrutin, and I. Sushko, “Codimension-2 border collision bifurcations in one-dimensional discontinuous  piecewise smooth maps,” Int. J. Bifurcat. Chaos, vol. 24, no. 2, p. 1450024, 2014.
    11. S. Heinrich et al., “Determinants of robustness in spindle assembly checkpoint signalling,” Nat. Cell. Biol., vol. 15, no. 11, pp. 1328–1339, 2014.
    12. A. Kramer, V. Stathopoulus, M. Girolami, and N. Radde, “MCMC_CLIB: An advanced MCMC sampling package for ode models.,” Bioinformatics, vol. 30, no. 20, pp. 2991–2992, 2014.
    13. A. Kramer, B. Calderhead, and N. Radde, “Hamiltonian Monte Carlo Methods for Efficient Parameter Estimation  in Steady State Dynamical Systems,” BMC Bioinf., vol. 15, p. 253, 2014.
    14. M. Löhning, M. Reble, J. Hasenauer, S. Yu, and F. Allgöwer, “Model predictive control using reduced order models: Guaranteed stability  for constrained linear systems,” J. Proc. Contr., vol. 24, no. 11, pp. 1647–1659, 2014.
    15. M. Ma, H. Chen, X. Liu, and F. Allgöwer, “Distributed model predictive load frequency control of multi-area  interconnected power system,” Int. J. Electrical Power & Energy Systems, vol. 62, pp. 289–298, 2014.
    16. M. A. Müller, D. Angeli, and F. Allgöwer, “On the performance of economic model predictive control with self-tuning  terminal cost,” J. Proc. Contr., vol. 24, no. 8, pp. 1179–1186, 2014.
    17. M. A. Müller, D. Angeli, and F. Allgöwer, “Transient average constraints in economic model predictive control,” Automatica, vol. 50, no. 11, pp. 2943–2950, 2014.
    18. M. A. Müller, D. Angeli, F. Allgöwer, R. Amrit, and J. B. Rawlings, “Convergence in economic model predictive control with average constraints,” Automatica, vol. 50, no. 12, pp. 3100–3111, 2014.
    19. M. A. Müller, D. Angeli, F. Allgöwer, R. Amrit, and J. B. Rawlings, “Convergence in economic model predictive control with average constraints,” Automatica, vol. 50, no. 12, pp. 3100–3111, 2014.
    20. N. Radde and J. Offtermatt, “Convergence of posteriors for structurally non-identified problems  using results from the theory of inverse problems,” J Inverse Ill-Pose P, vol. 22, no. 2, pp. 251–276, 2014.
    21. D. Radi, L. Gardini, and V. Avrutin, “The Role of Constraints in a Segregation Model: The Asymmetric Case,” Discrete Dynamics in Nature and Society, vol. 2014, p. Article ID 569296, 17 pages, 2014.
    22. D. Radi, L. Gardini, and V. Avrutin, “The Role of Constraints in a Segregation Model: The Symmetric Case,” Chaos, Solitons and Fractals, vol. 66, pp. 103–119, 2014.
    23. S. Schuler, U. Münz, and F. Allgöwer, “Decentralized state feedback control for interconnected systems with  application to power systems,” J. Proc. Contr., vol. 24, no. 2, pp. 379–388, 2014.
    24. G. Seyboth, J. Wu, J. Qin, C. Yu, and F. Allgöwer, “Collective Circular Motion of Unicycle Type Vehicles with Nonidentical  Constant Velocities,” IEEE Trans. Control of Network Systems, vol. 1, no. 2, pp. 167–176, 2014.
    25. K. Worthmann, M. Reble, L. Grüne, and F. Allgöwer, “The Role of Sampling for Stability and Performance in Unconstrained  Nonlinear Model Predictive Control,” SIAM J. Control Optim., vol. 52, no. 1, pp. 581–605, 2014.
    26. S. Yu, M. Reble, H. Chen, and F. Allgöwer, “Inherent robustness properties of quasi-infinite horizon nonlinear  model predictive control,” Automatica, vol. 50, no. 9, pp. 2269–2280, 2014.
    27. S. Trip, M. Bürger, and C. Persis, “An Internal Model Approach to Frequency Regulation in Inverter-based  Microgrids With Time-varying Voltages,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), 2014, pp. 223–228.
    28. A. Haupt et al., “Wireless Networking for Control,” in Control Theory of Digitally Networked Dynamic Systems, J. Lunze, Ed. Springer International Publishing, 2014, pp. 325–362.
    29. M. A. Müller and F. Allgöwer, “Distributed MPC for consensus and synchronization,” in Distributed MPC Made Easy, J. M. Maestre and R. Negenborn, Eds. Springer Verlag, 2014, pp. 89–100.
    30. F. Bayer, M. A. Müller, and F. Allgöwer, “Set-based Disturbance Attenuation in Economic Model Predictive Control,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 1898–1903.
    31. F. Bayer and F. Allgöwer, “Robust Economic Model Predictive Control with Linear Average Constraints,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 6707–6712.
    32. R. Blind and F. Allgöwer, “On the stabilizability of continuous-time systems over a packet based  communication system with loss and delay,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 6466–6471.
    33. F. D. Brunner, W. P. M. H. Heemels, and F. Allgöwer, “Robust Self-Triggered MPC for Constrained Linear Systems,” in Proc. European Control Conf. (ECC), Strasbourg, France, 2014, pp. 472–477.
    34. F. D. Brunner and F. Allgöwer, “Approximate Predictive Control of Polytopic Systems,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 11060--11066.
    35. F. D. Brunner, M. Lazar, and F. Allgöwer, “Computation of piecewise affine terminal cost functions for model  predictive control,” in Proc. 17th Int. Conf. Hybrid Systems: Computation and Control (HSCC), Berlin, Germany, 2014, pp. 1–10.
    36. M. Bürger, C. De Persis, and S. Trip, “An Internal Model Approach to (Optimal) Frequency Regulation in Power  Grids,” in Proc. 21st Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Groningen, The Netherlands, 2014, pp. 577–583.
    37. M. Bürger and C. De Persis, “Further Results About Dynamic Coupling for Nonlinear Output Agreement,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 1353–1358.
    38. M. Bürger, C. De Persis, and F. Allgöwer, “Optimal Pricing Control in Distribution Networks With Time-varying  Supply and Demand,” in Proc. 21st Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Groningen, The Netherlands, 2014, pp. 584–591.
    39. C. Feller and C. Ebenbauer, “Barrier function based linear model predictive control with polytopic  terminal sets,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 6683–6688.
    40. C. Feller and C. Ebenbauer, “Continuous-time linear MPC algorithms based on relaxed logarithmic  barrier functions,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 2481–2488.
    41. G. Goebel and F. Allgöwer, “Improved state dependent parametrizations including a piecewise linear  feedback for constrained linear MPC,” in Proc. American Control Conf. (ACC), Portland, OR, USA, 2014, pp. 4192–4197.
    42. G. Goebel and F. Allgöwer, “State Dependent Parametrizations for Nonlinear MPC,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 1005–1010.
    43. G. Goebel and F. Allgöwer, “Increasing performance of parametrizations for linear MPC via application  of a data mining algorithm,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 4932–4937.
    44. M. Lorenzen and M.-A. Belabbas, “Distributed local stabilization in formation control,” in Proc. European Control Conf. (ECC), Strasbourg, France, 2014, pp. 2914–2919.
    45. S. Michalowsky and C. Ebenbauer, “The multidimensional n-th order heavy ball method and its application  to extremum seeking,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 2660–2666.
    46. J. M. Montenbruck and F. Allgöwer, “Pinning Capital Stock and Gross Investment Rate in Competing Rationally  Managed Firms,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 10719–10724.
    47. J. M. Montenbruck, H.-B. Dürr, C. Ebenbauer, and F. Allgöwer, “Extremum Seeking and Obstacle Avoidance on the Special Orthogonal  Group,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 8229–8234.
    48. J. M. Montenbruck, H.-B. Dürr, C. Ebenbauer, and F. Allgöwer, “Extremum Seeking and Obstacle Avoidance on the Special Orthogonal Group,” in Proc.\ 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 8229–8234.
    49. M. A. Müller and F. Allgöwer, “Distributed economic MPC: a framework for cooperative control problems,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 1029–1034.
    50. M. A. Müller, D. Angeli, and F. Allgöwer, “Performance analysis of economic MPC with self-tuning terminal  cost,” in Proc. American Control Conf. (ACC), Portland, OR, USA, 2014, pp. 2845–2850.
    51. R. M. Schaich, M. A. Müller, and F. Allgöwer, “A distributed model predictive control scheme for networks with communication  failure,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 12004–12009.
    52. G. Seyboth and F. Allgöwer, “Synchronized model matching: a novel approach to cooperative control  of nonlinear multi-agent systems,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 1985–1990.
    53. G. Seyboth and F. Allgöwer, “Synchronized model matching: a novel approach to cooperative control of nonlinear multi-agent systems,” in Proc.\ 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 1985–1990.
    54. S. Waldherr, S. Zeng, and F. Allgöwer, “Identifiability of population models via a measure theoretical approach,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 1717–1722.
    55. J. Wu, V. Ugrinovskii, and F. Allgöwer, “Cooperative estimation for synchronization of heterogeneous multi-agent  systems using relative information,” in Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014, pp. 4662–4667.
    56. D. Zelazo and M. Bürger, “On the Definiteness of the Weighted Laplacian and its Connection  to Effective Resistance.,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 2895–2900.
    57. S. Zeng, S. Waldherr, and F. Allgöwer, “An inverse problem of tomographic type in population dynamics,” in Proc. 53rd IEEE Conf. Decision and Control (CDC), Los Angeles, CA, USA, 2014, pp. 1643–1648.
    58. D. Imig et al., “Simulations and predictions of TRAIL response in lung cancer cell  populations via mathematical modeling.” 2014.
    59. D. Imig, T. Strecker, N. Pollak, P. Scheurich, F. Allgöwer, and S. Waldherr, “Simulations and predictions of the response of heterogeneous cancer  cell populations to apoptosis induction.” 2014.
    60. K. Kuritz and F. Allgöwer, “Determining cell-cycle induced variations from snap-shot data sets.” 2014.
    61. R. Blind, “Optimization of the Communication System for Networked Control Systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2014.
    62. M. A. Müller, “Distributed and economic model predictive control: beyond setpoint  stabilization,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, 2014.
  7. 2013

    1. R. Blind and F. Allgöwer, “On Time-Triggered and Event-Based Control of Integrator Systems over  a Shared Communication System,” Mathematics of Control, Signals, and Systems, vol. 25, no. 4, pp. 517–557, 2013.
    2. R. Blind and F. Allgöwer, “On the Optimization of the Transport Layer for Networked Control  Systems,” at-Automatisierungstechnik, vol. 61, no. 7, pp. 495–505, 2013.
    3. M. Bürger, D. Zelazo, and F. Allgöwer, “Hierarchical Clustering of Dynamical Networks Using a Saddle-Point  Analysis,” IEEE Trans. Automat. Control, vol. 58, no. 1, pp. 113–124, 2013.
    4. H. B. Dürr, M. S. Stankovic, C. Ebenbauer, and K. H. Johansson, “Lie Bracket Approximation of Extremum Seeking Systems,” Automatica, vol. 49, no. 6, pp. 1538–1552, 2013.
    5. C. Feller, T. A. Johansen, and S. Olaru, “An improved algorithm for combinatorial multi-parametric quadratic  programming,” Automatica, vol. 49, no. 5, pp. 1370–1376, 2013.
    6. R. Krause et al., “Scientific workflows for bone remodelling simulations,” Proceedings in Applied Mathematics and Mechanics, 2013.
    7. M. A. Müller, D. Angeli, and F. Allgöwer, “Economic model predictive control with self-tuning terminal cost,” European J. Control, vol. 19, no. 5, pp. 408–416, 2013.
    8. M. Reble, D. E. Quevedo, and F. Allgöwer, “Control over Erasure Channels: Stochastic Stability and Performance  of Packetized Unconstrained Model Predictive Control,” Int. J. Robust and Nonlinear Control, vol. 23, no. 10, pp. 1151–1167, 2013.
    9. D. Schittler, F. Allgöwer, and R. J. De Boer, “A new model to simulate and analyze proliferating cell populations  in BrdU labeling experiments,” BMC Systems Biology (Suppl.: Selected articles from the 10th International  Workshop on Computational Systems Biology (WSCB) 2013), vol. 7(Suppl 1):S4, 2013.
    10. G. S. Schmidt, S. Michalowsky, C. Ebenbauer, and F. Allgöwer, “Global Output Regulation for the Rotational Dynamics of a Rigid Body,” at - Automatisierungstechnik, vol. 61, no. 8, pp. 567--581, 2013.
    11. S. Schuler, D. Schlipf, P. W. Cheng, and F. Allgöwer, “$\ell_1$-Optimal Control of Large Wind Turbines,” IEEE Trans. Cont. Sys. Tech., vol. 21, no. 4, pp. 1079–1089, 2013.
    12. G. Seyboth, D. V. Dimarogonas, and K. H. Johansson, “Event-based Broadcasting for Multi-agent Average Consensus,” Automatica, vol. 49, no. 1, pp. 245–252, 2013.
    13. C. Thomaseth, P. Weber, T. Hamm, K. Kashima, and R. N., “Modeling sphingomyelin synthase 1 driven reaction at the Golgi apparatus  can explain data by inclusion of a positive feedback mechanism,” J. Theor. Biol., vol. 337, pp. 174–180, 2013.
    14. P. Wieland, J. Wu, and F. Allgöwer, “On synchronous steady states and internal models of diffusively coupled  systems,” IEEE Trans. Automat. Control, vol. 58, no. 10, pp. 2591–2602, 2013.
    15. S. Yu, C. Maier, H. Chen, and F. Allgöwer, “Tube MPC scheme based on robust control invariant set with application  to Lipschitz nonlinear systems,” Syst. Contr. Lett., vol. 62, no. 2, pp. 194–200, 2013.
    16. D. Zelazo, M. Bürger, and F. Allgöwer, “A Finite-Time Dual Method For Negotiation Between Dynamical Systems,” SIAM J. Control Optim., vol. 51, no. 1, pp. 172–194, 2013.
    17. D. Zelazo, S. Schuler, and F. Allgöwer, “Performance and design of cycles in consensus networks,” Syst. Contr. Lett., vol. 62, no. 1, pp. 85–96, 2013.
    18. D. Zelazo, M. Bürger, and F. Allgöwer, “Dynamic negotiation under switching communication,” in Mathematical Systems Theory - Festschrift in Honor of Uwe Helmke  on the Occasion of his Sixtieth Birthday, K. Hüper and J. Trumpf, Eds. CreateSpace, 2013, pp. 479–500.
    19. F. Bayer, M. Bürger, and F. Allgöwer, “Discrete-time Incremental ISS: A Framework for Robust NMPC,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 2068–2073.
    20. F. Bayer, G. Notarstefano, and F. Allgöwer, “A Projected SQP Method for Nonlinear Optimal Control with Quadratic  Convergence,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 6463–6468.
    21. R. Blind and F. Allgöwer, “Retransmitting Lost Measurements to Improve Remote Estimation,” in Proc. American Control Conf. (ACC), Washington, D.C., USA, 2013, pp. 4154–4158.
    22. R. Blind and F. Allgöwer, “On the Joint Design of Controller and Routing for Networked Control  Systems,” in Proc. 4th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Koblenz, Germany, 2013, pp. 240–246.
    23. C. Breindl, M. Chaves, and F. Allgöwer, “A linear reformulation of Boolean optimization problems and structure  identification of gene regulation networks,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), 2013, pp. 733--738.
    24. F. D. Brunner, M. Lazar, and F. Allgöwer, “An Explicit Solution to Constrained Stabilization via Polytopic Tubes,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 7721–7727.
    25. F. D. Brunner, M. Lazar, and F. Allgöwer, “Stabilizing Linear Model Predictive Control: On the Enlargement of  the Terminal Set,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 511–517.
    26. M. Bürger, Z. D., and F. Allgöwer, “On the Steady-State Inverse-Optimality of Passivity-based Cooperative  Control.,” in Proc. 4th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Koblenz, 2013, pp. 138–143.
    27. M. Bürger, G. Notarstefano, and F. Allgöwer, “From Non-cooperative to Cooperative Distributed MPC: A Simplicial  Approximation Perspective,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 2795–2800.
    28. M. Bürger and C. De Persis, “Internal Models for nonlinear output agreement and optimal flow control,” in Proc. 9th IFAC Symp. Nonlinear Control Systems (NOLCOS), Toulouse, France, 2013, pp. 289–294.
    29. M. Bürger and C. De Persis, “Internal Models for nonlinear output agreement and optimal flow control,” in Proc.\ 9th IFAC Symp.\ Nonlinear Control Systems (NOLCOS), Toulouse, France, 2013, pp. 289–294.
    30. H. B. Dürr, M. S. Stankovic, D. V. Dimarogonas, C. Ebenbauer, and K. H. Johansson, “Obstacle Avoidance for an Extremum Seeking System using a Navigation  Function,” in Proc. American Control Conf. (ACC), Washington, D.C., USA, 2013, pp. 4068–4073.
    31. H. B. Dürr, C. Zeng, and C. Ebenbauer, “Saddle Point Seeking for Convex Optimization Problems,” in Proc. 9th IFAC Symp. Nonlinear Control Systems (NOLCOS), Toulouse, France, 2013, pp. 540–545.
    32. C. Feller and C. Ebenbauer, “Ein zeitkontinuierlicher Optimierungsalgorithmus für die modellprädiktive  Regelung linearer Systeme,” in Proc. 18. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung, Leibnitz, Austria, 2013, pp. 1–28.
    33. C. Feller and T. A. Johansen, “Explicit MPC of higher-order linear processes via combinatorial multi-parametric  programming,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 536–541.
    34. C. Feller and C. Ebenbauer, “A barrier function based continuous-time algorithm for linear model  predictive control,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 19–26.
    35. C. Feller and C. Ebenbauer, “Ein zeitkontinuierlicher Optimierungsalgorithmus für die modellprädiktive Regelung linearer Systeme,” in Proc.\ 18. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung, Leibnitz, Austria, 2013, pp. 1–28.
    36. G. Goebel and F. Allgöwer, “Obtaining and employing state dependent parametrizations of prespecified  complexity in constrained MPC,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 7077–7082.
    37. M. Lorenzen, M. Bürger, G. Notarstefano, and F. Allgöwer, “A Distributed Solution to the Adjustable Robust Economic Dispatch  Problem,” in Proc. 4th IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), 2013, pp. 75–80.
    38. M. Lorenzen, M. Bürger, G. Notarstefano, and F. Allgöwer, “A Distributed Solution to the Adjustable Robust Economic Dispatch Problem,” in Proc.\ 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), 2013, pp. 75–80.
    39. S. Michalowsky and C. Ebenbauer, “Swinging up the Stephenson-Kapitza pendulum,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 3981–3987.
    40. J. M. Montenbruck, M. Bürger, and F. Allgöwer, “Practical Cluster Synchronization of Heterogeneous Systems on Graphs  with Acyclic Topology,” in Proc. 52nd IEEE Conf. on Decision and Control (CDC), Florence, Italy, 2013, pp. 692–697.
    41. J. M. Montenbruck, G. S. Seyboth, and F. Allgöwer, “Practical and Robust Synchronization of Systems with Additive Linear  Uncertainties,” in Proc. 9th IFAC Symp. Nonlinear Control Systems (NOLCOS), Toulouse, France, 2013, pp. 743–748.
    42. J. M. Montenbruck, G. S. Seyboth, and F. Allgöwer, “Practical and Robust Synchronization of Systems with Additive Linear Uncertainties,” in Proc.\ 9th IFAC Symp.\ Nonlinear Control Systems (NOLCOS), Toulouse, France, 2013, pp. 743–748.
    43. M. A. Müller, D. Liberzon, and F. Allgöwer, “Norm-controllability, or how a nonlinear system responds to large  inputs,” in Proc. 9th IFAC Symp. Nonlinear Control Systems (NOLCOS), 2013, pp. 104–109.
    44. M. A. Müller, D. Angeli, and F. Allgöwer, “On convergence of averagely constrained economic MPC and necessity  of dissipativity for optimal steady-state operation,” in Proc. American Control Conf. (ACC), 2013, pp. 3147–3152.
    45. M. A. Müller, D. Angeli, and F. Allgöwer, “Economic model predictive control with transient average constraints,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 5119–5124.
    46. M. A. Müller, D. Angeli, and F. Allgöwer, “Economic model predictive control with self-tuning terminal weight,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 2044–2049.
    47. D. Schittler, F. Allgöwer, and S. Waldherr, “Multistability equivalence between gene regulatory networks of different  dimensionality,” in Proc. European Control Conf. (ECC), Zurich, Switzerland, 2013, pp. 3640–3645.
    48. G. S. Schmidt, C. Ebenbauer, and F. Allgöwer, “Output regulation for attitude control: a global approach,” in Proc. American Control Conf. (ACC), Washington, D.C., USA, 2013, pp. 5251–5256.
    49. S. Schuler, D. Zelazo, and F. Allgöwer, “Robust Design of Sparse Relative Sensing Networks,” in Proc. European Control Conference (ECC), Zurich, Switzerland, 2013, pp. 1860–1865.
    50. G. Seyboth and F. Allgöwer, “Clock Synchronization over Directed Graphs,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 6105–6111.
    51. J. Wu, J. Qin, B. Yu, and F. Allgöwer, “Leaderless synchronization of linear multi-agent systems under directed  switching topologies: an invariance approach,” in Proc. 52nd IEEE Conf. Decision and Control (CDC), Florence, Italy, 2013, pp. 6043–6048.
    52. K. Kuritz, N. Radde, and M. Olayioye, “Multi-scale modeling reveals membrane domain variations as mechanism  behind augmented ErbB receptor activation.” 2013.
    53. C. Thomaseth, P. Weber, T. Hamm, K. Kashima, and N. Radde, “Modeling sphingomyelin synthase 1 driven reaction at the Golgi apparatus  can explain data by inclusion of a positive feedback mechanism.” 2013.
    54. M. Bürger, “Duality and Approximation Methods for Cooperative Optimization and  Control,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2013.
    55. J. Hasenauer, “Modeling and parameter estimation for heterogeneous cell populations,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2013.
    56. M. Reble, “Model Predictive Control for Nonlinear Continuous-Time Systems with  and without Time-Delays,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2013.
    57. G. S. Schmidt, “Synchronization of Oscillators and Global Output Regulation for Rigid  Body Systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2013.
    58. D. Schittler, T. Jouini, F. Allgöwer, and S. Waldherr, “Generalization of the construction method for multistability-equivalent  gene regulatory networks to systems with multi-input multi-output  loopbreaking,” arXiv:1312.7250v2, 2013.
  8. 2012

    1. C. Böhm, M. Lazar, and F. Allgöwer, “Stability of periodically time-varying systems: Periodic Lyapunov  functions,” Automatica, vol. 48, no. 10, pp. 2663–2669, 2012.
    2. C. Böhm, M. Lazar, and F. Allgöwer, “Stability of periodically time-varying systems: Periodic Lyapunov functions,” Automatica, vol. 48, no. 10, pp. 2663–2669, 2012.
    3. M. Bürger, G. Notarstefano, F. Bullo, and F. Allgöwer, “A distributed simplex algorithm for degenerate linear programs and  multi-agent assignments,” Automatica, vol. 48, no. 9, pp. 2298–2304, 2012.
    4. M. Daub, S. Waldherr, F. Allgöwer, P. Scheurich, and G. Schneider, “Death wins against life in a spatially extended model of the caspase-3/8  feedback loop,” Biosystems, vol. 108, pp. 45–51, 2012.
    5. J. Hasenauer, D. Schittler, and F. Allgöwer, “Analysis and simulation of division- and label-structured population  models,” Bulletin of Mathematical Biology, vol. 74, no. 11, pp. 2692–2732, 2012.
    6. J. Hasenauer, J. Heinrich, M. Doszczak, P. Scheurich, D. Weiskopf, and F. Allgöwer, “A visual analytics approach for models of heterogeneous cell populations,” EURASIP J. Bioinformatics and Systems Biology, vol. 2012, no. 2012, p. 4, 2012.
    7. J. Hasenauer, M. Löhning, M. Khammash, and F. Allgöwer, “Dynamical optimization using reduced order models: A method to  guarantee performance.,” J. Proc. Contr., vol. 22, no. 8, pp. 1490–1501, 2012.
    8. R. Krause, D. Schittler, S. Waldherr, F. Allgöwer, B. Markert, and W. Ehlers, “Remodelling Processes in Bone: A Biphasic Porous Media Model,” Proceedings in Applied Mathematics and Mechanics, vol. 12, no. 1, pp. 131–132, 2012.
    9. M. A. Müller and A. D. Dominguez-Garcia, “Fault coverage modeling in nonlinear dynamical systems,” Automatica, vol. 48, no. 7, pp. 1372–1379, 2012.
    10. M. A. Müller, M. Reble, and F. Allgöwer, “Cooperative control of dynamically decoupled systems via distributed  model predictive control,” Int. J. Robust and Nonlinear Control, vol. 22, no. 12, pp. 1376–1397, 2012.
    11. M. A. Müller and F. Allgöwer, “Improving performance in model predictive control: Switching cost  functionals under average dwell-time,” Automatica, vol. 48, no. 2, pp. 402–409, 2012.
    12. M. A. Müller and D. Liberzon, “Input/output-to-state stability and state-norm estimators for switched  nonlinear systems,” Automatica, vol. 48, no. 9, pp. 2029–2039, 2012.
    13. M. A. Müller, P. Martius, and F. Allgöwer, “Model predictive control of switched nonlinear systems under average  dwell-time,” J. Proc. Contr., vol. 22, no. 9, pp. 1702–1710, 2012.
    14. N. Radde, “Analyzing fixed points of intracellular regulation networks with  complex feedback topology,” BMC Sys. Biol., vol. 6, no. 57, 2012.
    15. M. Reble and F. Allgöwer, “Unconstrained Model Predictive Control and Suboptimality Estimates  for Nonlinear Continuous-Time Systems,” Automatica, vol. 48, no. 8, pp. 1812–1817, 2012.
    16. P. Weber, A. Kramer, C. Dingler, and N. Radde, “Trajectory-oriented Bayesian experiment design versus Fisher A-optimal  design: an in depth comparison study,” Bioinformatics, vol. 28, no. 18, pp. i535–i541, 2012.
    17. S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “Model predictive control of constrained LPV systems,” Int. J. Control, vol. 85, no. 6, pp. 671–683, 2012.
    18. D. Zelazo, R. Dai, and M. Mesbahi, “An energy management system for off-grid power systems,” Energy Systems, vol. 3, no. 2, pp. 153--179, 2012.
    19. D. Zelazo, R. Dai, and M. Mesbahi, “An energy management system for off-grid power systems,” Energy Systems, vol. 3, no. 2, pp. 153--179, 2012.
    20. M. Reble and F. Allgöwer, “Design of Terminal Cost Functionals and Terminal Regions for Model  Predictive Control of Nonlinear Time-Delay Systems,” in Time Delay Systems: Methods, Applications and New Trends, vol. 423, R. Sipahi, T. Vyhlidal, P. Pepe, and S.-I. Niculescu, Eds. Springer Berlin / Heidelberg, 2012, pp. 355–366.
    21. S. Waldherr, F. Allgöwer, E. W. Jacobsen, and S. Streif, “Robustness and adaptation of biological networks under kinetic perturbations,” in Control Theory: Mathematical Perspectives on Complex Networked Systems, no. 12/2012, F. Allgöwer, V. Blondel, and U. Helmke, Eds. Oberwolfach, Germany: Mathematisches Forschungsinstitut Oberwolfach, 2012, pp. 62–63.
    22. F. Bayer and J. Hauser, “Trajectory Optimization for Vehicles in a Constrained Environment,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 5625–5630.
    23. R. Blind and F. Allgöwer, “Is it Worth to Retransmit Lost Packets in Networked Control Systems?,” in Proc. 51th IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 1368–1373.
    24. R. Blind and F. Allgöwer, “The Performance of Event-Based Control for Scalar Systems with Packet  Losses,” in Proc. 51th IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 6572–6576.
    25. C. Breindl, M. Chaves, J. L. Gouzé, and F. Allgöwer, “Structure estimation for unate Boolean models of gene regulation  networks,” in Proc. 16th IFAC Symp. System Identification (SYSID), Brussels, Belgium, 2012, pp. 1725--1730.
    26. F. D. Brunner, H. B. Dürr, and C. Ebenbauer, “Feedback Design for Multi-Agent Systems: A Saddle Point Approach,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 3783–3789.
    27. M. Bürger, D. Zelazo, and F. Allgöwer, “Combinatorial Insights and Robustness Analysis for Clustering in  Dynamical Networks,” in Proc. American Control Conf. (ACC), Montreal, Canada, 2012, pp. 454–459.
    28. M. Bürger, G. Notarstefano, and F. Allgöwer, “Distributed Robust Optimization via Cutting-Plane Consensus,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 7457–7463.
    29. B. W. Carabelli et al., “Exact Convex Formulations of Network-Oriented Optimal Operator Placement,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 3777–3782.
    30. H. B. Dürr and C. Ebenbauer, “On a Class of Smooth Optimization Algorithms with Applications in  Control,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Noordwijkerhout, The Netherlands, 2012, pp. 291–298.
    31. H. B. Dürr, E. Saka, and C. Ebenbauer, “A Smooth Vector Field for Quadratic Programming,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 2515–2520.
    32. C. Feller, T. A. Johansen, and S. Olaru, “Combinatorial multi-parametric quadratic programming with saturation  matrix based pruning,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 4562–4567.
    33. M. A. Müller, B. Schürmann, and F. Allgöwer, “Robust cooperative control of dynamically decoupled systems via distributed  MPC,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Noordwijkerhout, The Netherlands, 2012, pp. 412–417.
    34. M. A. Müller and F. Allgöwer, “Robustness of steady-state optimality in economic model predictive  control,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 1011–1016.
    35. M. A. Müller, D. Liberzon, and F. Allgöwer, “Relaxed conditions for norm-controllability of nonlinear systems,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 314–319.
    36. N. Radde, “Identification of feedback circuits that are connected to multiple  fixed points in biological networks,” in Proc. 8th Int. Workshop on Computational Systems Biology (WCSB), Ulm, Germany, 2012, pp. 59–62.
    37. M. Reble, D. E. Quevedo, and F. Allgöwer, “A Unifying Framework for Stability in MPC using a Generalized Integral  Terminal Cost,” in Proc. American Control Conf. (ACC), Montreal, Canada, 2012, pp. 1211–1216.
    38. M. Reble, D. E. Quevedo, and F. Allgöwer, “Improved Stability Conditions for Unconstrained Nonlinear Model Predictive  Control by using Additional Weighting Terms,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 2625–2630.
    39. D. Schittler, J. Hasenauer, and F. Allgöwer, “A model for proliferating cell populations that accounts for cell  types,” in Proc. 9th Int. Workshop on Computational Systems Biology (WCSB), Ulm, Germany, 2012, pp. 84–87.
    40. D. Schittler, J. Hasenauer, and F. Allgöwer, “A model for proliferating cell populations that accounts for cell types,” in Proc.\ 9th Int.\ Workshop on Computational Systems Biology (WCSB), Ulm, Germany, 2012, pp. 84–87.
    41. G. S. Schmidt, C. Ebenbauer, and F. Allgöwer, “A solution for a class of output regulation problems on SO(n),” in Proc. American Control Conf. (ACC), Montreal, Canada, 2012, pp. 1773–1779.
    42. G. S. Schmidt, C. Ebenbauer, and F. Allgöwer, “A solution for a class of output regulation problems on SO(n),” in Proc.\ American Control Conf.\ (ACC), Montreal, Canada, 2012, pp. 1773–1779.
    43. S. Schuler, D. Zelazo, and F. Allgöwer, “Design of Sparse Relative Sensing Networks,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 2749–2754.
    44. S. Schuler, U. Münz, and F. Allgöwer, “Decentralized State Feedback Control for Interconnected Process Systems,” in Proc. 8th IFAC Symposium on Advanced Control of Chemical Processes  (AdChem), Singapore, 2012, pp. 1–10.
    45. G. Seyboth, G. S. Schmidt, and F. Allgöwer, “Cooperative Control of Linear Parameter-Varying Systems,” in Proc. American Control Conf. (ACC), Montreal, Canada, 2012, pp. 2407–2412.
    46. G. Seyboth, G. S. Schmidt, and F. Allgöwer, “Output Synchronization of Linear Parameter-varying Systems via Dynamic  Couplings,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 5128–5133.
    47. G. Seyboth, D. V. Dimarogonas, K. H. Johansson, and F. Allgöwer, “Static Diffusive Couplings in Heterogeneous Linear Networks,” in Proc. 3rd IFAC Workshop on Distributed Estimation and Control in  Networked Systems (NecSys), Santa Barbara, CA, USA, 2012, pp. 258–263.
    48. S. Waldherr, J. Hasenauer, and F. Allgöwer, “Set based uncertainty analysis and parameter estimation of biological  networks with the BioSDP toolbox,” in Proc. 9th Int. Workshop on Computational Systems Biology (WCSB), Ulm, Germany, 2012.
    49. J. Wu and F. Allgöwer, “A Constructive Approach to Synchronization Using Relative Information,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 5960–5965.
    50. D. Zelazo, A. Franchi, F. Allgöwer, H. H. Bülthoff, and P. Robuffo Giordano, “Rigidity Maintenance Control for Multi-robot Systems,” in Proc. Robotics: Science and Systems, Sydney, Australia, 2012.
    51. D. Zelazo, S. Schuler, and F. Allgöwer, “Cycles and Sparse Design of Consensus Networks,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 3808–3813.
    52. D. Zelazo and F. Allgöwer, “Growing Optimally Rigid Formations,” in Proc. American Control Conf. (ACC), Montreal, Canada, 2012, pp. 3901–3906.
    53. D. Zelazo and F. Allgöwer, “Eulerian Consensus Networks,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 4715–4720.
    54. H. Perfahl, A. Bronikowski, M. Doszczak, P. Scheurich, and M. Reuss, “Multiscale Modelling of TRAIL Therapy Applied to Multicellular Spheroids.” 2012.
    55. H. Perfahl, M. R. Owen, T. Alarcon, P. K. Maini, H. M. Byrne, and M. Reuss, “3D Modelling of Vascular Network Formation by an Overlapping-Sphere  Approach.” 2012.
    56. H. Perfahl, A. Bronikowski, M. Doszczak, P. Scheurich, and M. Reuss, “Multiscale Modelling of TRAIL Therapy Applied to Multicellular Spheroids.” 2012.
    57. D. Schittler, G. Vacun, H. Walles, J. Hansmann, F. Allgöwer, and S. Waldherr, “Towards a dynamical model of gene regulation in mesenchymal stem  cell differentiation.” 2012.
    58. S. Waldherr, R. Buschow, J. Isensee, F. Allgöwer, and T. Hucho, “Cellular heterogeneity in neuronal pain sensing.” 2012.
  9. 2011

    1. C. Breindl, S. Waldherr, D. M. Wittmann, F. J. Theis, and F. Allgöwer, “Steady state robustness of qualitative gene regulation networks,” Int. J. Robust and Nonlinear Control, vol. 21, no. 15, pp. 1742--1758, 2011.
    2. I. Couchman, E. Kerrigan, and C. Böhm, “Model reduction of homogeneous-in-the-state bilinear systems with  input constraints,” Automatica, vol. 47, no. 4, pp. 761–768, 2011.
    3. G. Goebel, U. Münz, and F. Allgöwer, “$L_2$-Gain-based controller design for linear systems  with distributed input delay,” IMA J. of Mathematical Control and Information, vol. 28, no. 2, pp. 225–237, 2011.
    4. J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgöwer, “Analysis of heterogeneous cell populations: A density-based modeling  and identification framework,” J. Proc. Contr., vol. 21, no. 10, pp. 1417–1425, 2011.
    5. J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgöwer, “Identification of models of heterogeneous cell populations from population  snapshot data,” BMC Bioinf., vol. 12, p. 125, 2011.
    6. R. Krause et al., “Bone remodelling: A combined biomechanical and systems-biological  challenge,” PAMM, vol. 11, no. 1, pp. 99--100, 2011.
    7. C. Maier, C. Böhm, F. Deroo, and F. Allgöwer, “Predictive Control for Polynomial Systems Subject to State and Input  Constraints,” at-Automatisierungstechnik, vol. 59, no. 8, pp. 479--488, 2011.
    8. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Robust Consensus Controller Design for Nonlinear Relative Degree  Two Multi-Agent Systems With Communication Constraints,” IEEE Trans. Autom. Control, vol. 56, no. 1, pp. 145–151, 2011.
    9. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Consensus in Multi-Agent Systems with Coupling Delays and Switching  Topology,” IEEE Trans. Autom. Control, vol. 56, no. 12, pp. 2976–2982, 2011.
    10. N. Radde, “The role of feedback mechanisms in biological network models - A  tutorial,” Asian J. Control, vol. 13, no. 5, pp. 1–14, 2011.
    11. M. Reble, R. M. Esfanjani, S. K. Y. Nikravesh, and F. Allgöwer, “Model Predictive Control of Constrained Nonlinear Time-Delay Systems,” IMA J. of Mathematical Control and Information, vol. 28, no. 2, pp. 183–201, 2011.
    12. M. Schliemann, E. Bullinger, E. Borchers, F. Allgöwer, R. Findeisen, and P. Scheurich, “Heterogeneity Reduces Sensitivity of Cell Death for TNF-Stimuli,” BMC Sys. Biol., vol. 5, no. 1, p. 204, 2011.
    13. K. Schmidt and C. Breindl, “Maximally Permissive Hierarchical Control of Decentralized Discrete  Event Systems,” IEEE Trans. Autom. Control, vol. 56, no. 4, pp. 723–737, 2011.
    14. S. Schuler, P. Li, J. Lam, and F. Allgöwer, “Design of Structured Dynamic Output Feedback Controllers for Interconnected  Systems,” Int. J. Control, vol. 84, no. 12, pp. 2081--2091, 2011.
    15. R. Steuer, S. Waldherr, V. Sourjik, and M. Kollmann, “Robust Signal Processing in Living Cells,” PLoS Comp. Biol., vol. 7, no. 11, p. e1002218, 2011.
    16. S. Waldherr, D. Dylus, and F. Allgöwer, “Bifurcation search via feedback loop breaking in biochemical signaling  pathways with time delay,” Asian J. Control, vol. 13, no. 5, pp. 691--700, 2011.
    17. S. Waldherr and F. Allgöwer, “Robust stability and instability of biochemical networks with parametric  uncertainty,” Automatica, vol. 47, pp. 1139–1146, 2011.
    18. P. Wieland, R. Sepulchre, and F. Allgöwer, “An internal model principle is necessary and sufficient for linear  output synchronization,” Automatica, vol. 47, no. 5, pp. 1068–1074, 2011.
    19. P. Wieland, R. Sepulchre, and F. Allgöwer, “An internal model principle is necessary and sufficient for linear output synchronization,” Automatica, vol. 47, no. 5, pp. 1068–1074, 2011.
    20. D. Zelazo and M. Mesbahi, “Graph-Theoretic Analysis and Synthesis of Relative Sensing Networks,” IEEE Trans. Autom. Control, vol. 56, no. 5, pp. 971–982, 2011.
    21. D. Zelazo and M. Mesbahi, “Edge Agreement: Graph-Theoretic Performance Bounds and Passivity  Analysis,” IEEE Trans. Autom. Control, vol. 56, no. 3, pp. 554–555, 2011.
    22. S. Waldherr, J. Hasenauer, M. Doszczak, P. Scheurich, and F. Allgöwer, “Global uncertainty analysis for a model of TNF-induced NF-$\kappa$B  signalling,” in Advances in the Theory of Control, Signals and Systems with Physical  Modeling, vol. 407, J. Levine and P. Müllhaupt, Eds. Springer Berlin / Heidelberg, 2011, pp. 365--377.
    23. F. Bayer, M. Bürger, M. Guay, and F. Allgöwer, “On State-Constrained Control of a CSTR,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 6079–6084.
    24. R. Blind and F. Allgöwer, “Analysis of Networked Event-Based Control with a Shared Communication  Medium: Part I - Pure ALOHA,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 10092–10097.
    25. R. Blind and F. Allgöwer, “On the Optimal Sending Rate for Networked Control Systems with a  Shared Communication Medium,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 4704–4709.
    26. R. Blind and F. Allgöwer, “Analysis of Networked Event-Based Control with a Shared Communication  Medium: Part II - Slotted ALOHA,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 8830–8835.
    27. R. Blind and F. Allgöwer, “On the Optimal Sending Rate for Networked Control Systems with a Shared Communication Medium,” in Proc.\ 50th IEEE Conf.\ Decision and Control (CDC), European Control Conf.\ (ECC), Orlando, FL, USA, 2011, pp. 4704–4709.
    28. C. Breindl, D. Schittler, S. Waldherr, and F. Allgöwer, “Structural requirements and discrimination of cell differentiation  networks,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 11767–11772.
    29. B. Briegel, D. Zelazo, M. Bürger, and F. Allgöwer, “On the Zeros of Consensus Networks,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 1890–1895.
    30. C. Böhm, S. Yu, and F. Allgöwer, “Moving horizon $H_ınfty$ control of constrained  periodically time-varying systems,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 10156–10161.
    31. M. Bürger, D. Zelazo, and F. Allgöwer, “Network Clustering: A Dynamical Systems and Saddle-Point Perspective,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 7825–7830.
    32. M. Bürger, G. Notarstefano, F. Allgöwer, and F. Bullo, “A distributed simplex algorithm and the multi-agent assignment problem,” in Proc. American Control Conf. (ACC), San Francisco, CA, USA, 2011, pp. 2639–2644.
    33. M. Bürger, G. Notarstefano, and F. Allgöwer, “Locally Constrained Decision Making via Two-Stage Distributed Simplex,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 5911–5916.
    34. M. Bürger, G. Notarstefano, and F. Allgöwer, “Locally Constrained Decision Making via Two-Stage Distributed Simplex,” in Proc.\ 50th IEEE Conf.\ Decision and Control (CDC), European Control Conf.\ (ECC), Orlando, FL, USA, 2011, pp. 5911–5916.
    35. F. Deroo, C. Maier, C. Böhm, and F. Allgöwer, “Offline NMPC for continuous-time systems using sum of squares,” in Proc. American Control Conf. (ACC), San Francisco, CA, USA, 2011, pp. 5163–5168.
    36. H. B. Dürr, S. Zeng, and C. Ebenbauer, “Ein nichtlineares System zum Lösen von Sattelpunktproblemen  und Linearen Programmen,” in 17. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung, 2011.
    37. H. B. Dürr, M. Stankovic, and K. H. Johansson, “Distributed Positioning of Autonomous Mobile Sensors with Application  to the Coverage Problem,” in Proc. American Control Conf. (ACC), San Francisco, CA, USA, 2011, pp. 4822–4827.
    38. H. B. Dürr, M. Stankovic, and K. H. Johansson, “A Lie Bracket Approximation for Extremum Seeking Vehicles,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 11393–11398.
    39. H. B. Dürr and C. Ebenbauer, “A Smooth Vector Field for Saddle Point Problems,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 4654–4660.
    40. J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgöwer, “Parameter estimation and uncertainty analysis for models of heterogeneous  cell populations,” in Proc. 12th Int. Conf. Systems Biology (ICSB), Heidelberg/Mannheim, Germany, 2011.
    41. J. Hasenauer, K. Erbertseder, M. Doszczak, R. Helmig, P. Scheurich, and F. Allgöwer, “Towards a multi-scale model for the therapeutic action of TRAIL  in lung carcinoma,” in Proc. 12th Int. Conf. Systems Biology (ICSB), Heidelberg/Mannheim, Germany, 2011.
    42. J. Hasenauer, C. Andres, T. Hucho, and F. Allgöwer, “A threshold-free method for assessing the responsiveness of heterogeneous  populations: DRG-neurons as a case study,” in Proc. 8th Int. Workshop on Computational Systems Biology (WCSB), Zürich, Switzerland, 2011, p. 209.
    43. J. Hasenauer, J. Heinrich, M. Doszczak, P. Scheurich, D. Weiskopf, and F. Allgöwer, “Visualization methods and support vector machines as tools for determining  markers in models of heterogeneous populations: Proapoptotic signaling  as a case study,” in Proc. 8th Int. Workshop on Computational Systems Biology (WCSB), Zürich, Switzerland, 2011, pp. 61–64.
    44. A. Joos, M. A. Müller, D. Baumgärtner, W. Fichter, and F. Allgöwer, “Nonlinear Predictive Control Based on Time-Domain Simulation for  Automatic Landing,” in Proc. AIAA Guidance, Navigation, and Control Conf., Portland, OR, USA, 2011, vol. 2, pp. 1619–1633.
    45. K. Kashima, A. Papachristodoulou, and F. Allgöwer, “Connection Profile Robustness in a Heterogeneous Network of Piecewise  Affine FitzHugh-Nagumo Models,” in Proc. SICE Annual Conf., Tokyo, Japan, 2011, pp. 2093–2098.
    46. M. Kögel, R. Blind, F. Allgöwer, and R. Findeisen, “Optimal and optimal-linear control over lossy, distributed networks,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 13239–13244.
    47. M. Löhning, J. Hasenauer, M. Khammash, and F. Allgöwer, “Optimierung mittels reduzierter Modelle mit garantierter Güte,” in Tagungsband Workshop GMA-Fachausschuss 1.30 ``Modellbildung, Identifikation  und Simulation in der Automatisierungstechnik’’, 2011.
    48. M. Löhning, J. Hasenauer, and F. Allgöwer, “Trajectory-based model reduction of nonlinear biochemical networks  employing the observability normal form,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 10442–10447.
    49. M. Löhning, J. Hasenauer, and F. Allgöwer, “Steady state stability preserving nonlinear model reduction using  sequential convex optimization,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 7158–7163.
    50. M. A. Müller, M. Reble, and F. Allgöwer, “A general distributed MPC framework for cooperative control,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 7987–7992.
    51. M. A. Müller, D. Liberzon, and F. Allgöwer, “On norm-controllabilty of nonlinear systems,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 1741–1746.
    52. M. A. Müller and F. Allgöwer, “Model predictive control of switched nonlinear systems under average  dwell-time,” in Proc. American Control Conf. (ACC), San Francisco, CA, USA, 2011, pp. 5169–5174.
    53. M. Reble and F. Allgöwer, “Unconstrained Nonlinear Model Predictive Control and Suboptimality  Estimates for Continuous-Time Systems,” in Proc. 18th IFAC World Congress, Milan, Italy, 2011, pp. 6733–6738.
    54. M. Reble, M. A. Müller, and F. Allgöwer, “Unconstrained Model Predictive Control and Suboptimality Estimates  for Nonlinear Time-Delay Systems,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 7599–7604.
    55. M. Reble, D. E. Quevedo, and F. Allgöwer, “Stochastic Stability and Performance Estimates of Packetized Unconstrained  Model Predictive Control for Networked Control Systems,” in Proc. 9th IEEE Int. Conf. Control and Automation, Santiago, Chile, 2011, pp. 171–176.
    56. M. Reble, F. D. Brunner, and F. Allgöwer, “Model Predictive Control for Nonlinear Time-Delay Systems without  Terminal Constraint,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 9254–9259.
    57. M. Reble, F. D. Brunner, and F. Allgöwer, “Model Predictive Control for Nonlinear Time-Delay Systems without Terminal Constraint,” in Proc.\ 18th IFAC World Congress, Milano, Italy, 2011, pp. 9254–9259.
    58. D. Schittler, J. Hasenauer, and F. Allgöwer, “A generalized population model for cell proliferation: Integrating  division numbers and label dynamics,” in Proc. 8th Int. Workshop on Computational Systems Biology (WCSB), Zürich, Switzerland, 2011, pp. 165–168.
    59. G. S. Schmidt, C. Ebenbauer, and F. Allgöwer, “Observability Properties of the Periodic Toda Lattice,” in Proc. 9th IEEE Int. Conf. Control and Automation, Santiago, Chile, 2011, pp. 704–709.
    60. S. Schuler, M. D. Gruhler, U. Münz, and F. Allgöwer, “Design of Structured Static Output Feedback Controllers,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 271–276.
    61. S. Schuler, C. Ebenbauer, and F. Allgöwer, “$\ell_0$-System Gain and $\ell_1$-Optimal Control,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 9230–9235.
    62. G. Seyboth, D. V. Dimarogonas, and K. H. Johansson, “Control of Multi-Agent Systems via Event-based Communication,” in Proc. 18th IFAC World Congress, Milan, Italy, 2011, pp. 10086–10091.
    63. P. Weber, J. Hasenauer, F. Allgöwer, and N. Radde, “Parameter estimation and identifiability of biological networks using  relative data,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 11648–11653.
    64. S. Yu, H. Chen, and F. Allgöwer, “Tube MPC scheme based on robust control invariant set with application  to Lipschitz nonlinear systems,” in Proc. 50th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Orlando, FL, USA, 2011, pp. 2650–2655.
    65. S. Yu, M. Reble, H. Chen, and F. Allgöwer, “Inherent robustness properties of quasi-infinite horizon NMPC,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 179–184.
    66. D. Zelazo, M. Bürger, and F. Allgöwer, “A Distributed Real-Time Algorithm for Preference-Based Agreement,” in Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 8933–8938.
    67. J. Hasenauer, S. Waldherr, M. Doszczak, P. Scheurich, N. Radde, and F. Allgöwer, “From single-cell models to mechanicstic population descriptions -  A modeling, simulation, and analysis framework.” 2011.
    68. M. A. Müller, M. Reble, and F. Allgöwer, “Distributed MPC for cooperative control.” 2011.
    69. D. Schittler, C. Breindl, and F. Allgöwer, “Model selection of networks that are robust against kinetic uncertainties.” 2011.
    70. D. Schittler, G. Vacun, J. Hansmann, F. Allgöwer, and S. Waldherr, “Modeling gene regulatory networks of mesenchymal stem cell differentiation.” 2011.
    71. S. Waldherr and F. Allgöwer, “Robustness analysis of biomolecular networks via polynomial programming.” May-2011.
    72. S. Waldherr and F. Allgöwer, “Reliable ovarian follicle development through a stochastic bistable  switch with cell-cell interactions.” 2011.
    73. C. Böhm, “Predictive Control Using Semi-definite Programming - Efficient Approaches  for Periodic Systems and Lur’e Systems,” University of Stuttgart, Stuttgart, Germany, 2011.
    74. S. Yu, “Robust Model Predictive Control of Constrained Systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2011.
  10. 2010

    1. A. Arsie and C. Ebenbauer, “Locating omega-limit sets using height functions,” Journal of Differential Equations, vol. 248, pp. 2458–2469, 2010.
    2. C. Böhm, R. Findeisen, and F. Allgöwer, “Robust control of constrained sector bounded Lur’e systems with  applications to nonlinear model predictive control,” Dynamics of Continuous, Discrete and Impulsive Systems, vol. 17, no. 6, pp. 935--958, 2010.
    3. M. Bürger and M. Guay, “Robust Constraint Satisfaction for Continuous Time Nonlinear Systems  in Strict Feedback Form,” IEEE Trans. Autom. Control, vol. 55, no. 11, pp. 2597–2601, 2010.
    4. J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen, “Guaranteed steady state bounds for uncertain (bio-)chemical processes  using infeasibility certificates,” J. Proc. Contr., vol. 20, no. 9, pp. 1076–1083, 2010.
    5. J. Hasenauer, S. Waldherr, K. Wagner, and F. Allgöwer, “Parameter identification, experimental design and model falsification  for biological network models using semidefinite programming,” IET Systems Biology, vol. 4, no. 2, pp. 119–130, 2010.
    6. J. Hasenauer, S. Waldherr, N. Radde, M. Doszczak, P. Scheurich, and F. Allgöwer, “A maximum likelihood estimator for parameter distributions in heterogeneous  cell populations,” Procedia Computer Science, vol. 1, no. 1, pp. 1649–1657, 2010.
    7. J.-S. Kim and F. Allgöwer, “A Nonlinear Synchronization Scheme for Hindmarsh-Rose Models,” J. Electrical Engineering and Technology, vol. 5, no. 1, pp. 163–170, 2010.
    8. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Delay Robustness in Consensus Problems,” Automatica, vol. 46, no. 8, pp. 1252–1265, 2010.
    9. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Robust Rendezvous of Heterogeneous Euler-Lagrange Systems on Packet-Switched  Networks,” at-Automatisierungstechnik, vol. 58, no. 4, pp. 184–191, 2010.
    10. A. Papachristodoulou, A. Jadbabaie, and U. Münz, “Effects of Delay in Multi-Agent Consensus and Oscillator Synchronization,” IEEE Trans. Autom. Control, vol. 55, no. 6, pp. 1471–1477, 2010.
    11. N. Radde, “Fixed point characterization of biological networks with complex  graph topology,” Bioinformatics, vol. 26, no. 22, pp. 2874–2880, 2010.
    12. D. Schittler, J. Hasenauer, F. Allgöwer, and S. Waldherr, “Cell differentiation modeled via a coupled two-switch regulatory  network,” Chaos, vol. 20, no. 4, pp. 1–9, 2010.
    13. S. Waldherr, J. Wu, and F. Allgöwer, “Bridging time scales in cellular decision making with a stochastic  bistable switch,” BMC Sys. Biol., vol. 4, p. 108, 2010.
    14. P. Wieland, J.-S. Kim, and F. Allgöwer, “On topology and dynamics of consensus among linear high-order agents,” Int. J. Systems Science, vol. 42, no. 10, pp. 1831–1842, 2010.
    15. L. Del Re, F. Allgöwer, L. Glielmo, C. Guardiola, and I. Kolmanovsky, Eds., Automotive Model Predictive Control, vol. 402. Springer Berlin / Heidelberg, 2010.
    16. L. Grüne, S. Sager, F. Allgöwer, H. G. Bock, and M. Diehl, “Predictive planning and systematic action -- on the control of technical  processes,” in Production Factor Mathematics, M. Grötschel, K. Lucas, and V. Mehrmann, Eds. Springer, 2010, pp. 9–37.
    17. O. Ajala, S. Schuler, and F. Allgöwer, “$\ell_ınfty$-Gain Controller Order Reduction for Discrete-Time  Systems,” in Proc. American Control Conf. (ACC), Baltimore, MD, USA, 2010, pp. 329–334.
    18. C. Breindl, S. Waldherr, and F. Allgöwer, “A robustness measure for the stationary behavior of qualitative gene  regulation networks,” in Proc. 11th Symp. Comput. Appl. Biotechnol. (CAB), Leuven, Belgium, 2010, pp. 36–41.
    19. C. Breindl, S. Waldherr, and F. Allgöwer, “A robustness measure for the stationary behavior of qualitative gene regulation networks,” in Proc.\  11th Symp.\ Comput.\ Appl.\  Biotechnol.\ (CAB), Leuven, Belgium, 2010, pp. 36–41.
    20. C. Böhm and F. Allgöwer, “Efficient offline model predictive control of constrained nonlinear  periodic systems,” in Proc. IFAC Workshop on Periodic Control Systems (PSYCO), Antalya, Turkey, 2010.
    21. C. Böhm, M. Lazar, and F. Allgöwer, “A relaxation of Lyapunov conditions and controller synthesis for  discrete-time periodic systems,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 3277--3282.
    22. C. Böhm, M. Lazar, and F. Allgöwer, “Stability analysis of periodically time-varying systems using periodic  Lyapunov functions,” in Proc. IFAC Workshop on Periodic Control Systems (PSYCO), Antalya, Turkey, 2010.
    23. M. Bürger, G. S. Schmidt, and F. Allgöwer, “Preference Based Group Agreement in Cooperative Control,” in Proc. 8th IFAC Symp. Nonlinear Control Systems (NOLCOS), Bologna, Italy, 2010, pp. 149–154.
    24. I. Couchman, E. Kerrigan, and C. Böhm, “Model reduction of homogeneous-in-the-state bilinear systems with  input constraints,” in Proc. American Control Conf. (ACC), Baltimore, Maryland, USA, 2010, pp. 2718--2723.
    25. C. Ebenbauer, “Linear Matrix Inequalities for Normalizing Matrices,” in Proc. 19th Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Budapest, Hungary, 2010, pp. 1375–1379.
    26. A. Freuer, M. Reble, C. Böhm, and F. Allgöwer, “Efficient Model Predictive Control for Linear Periodic Systems,” in Proc. 19th Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Budapest, Hungary, 2010, pp. 1403–1409.
    27. G. Goebel, U. Münz, and F. Allgöwer, “Stabilization of linear systems with distributed input delay,” in Proc. American Control Conf. (ACC), Baltimore, Maryland, USA, 2010, pp. 5800--5806.
    28. J. Hasenauer, C. Breindl, S. Waldherr, and F. Allgöwer, “Approximative classification of regions in parameter spaces of nonlinear  ODEs yielding different qualitative behavior,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 4114–4119.
    29. J. Hasenauer, S. Waldherr, M. Doszczak, P. Scheurich, and F. Allgöwer, “Density-based modeling and identification of biochemical networks  in cell populations,” in Proc. 9th IFAC Symp. Dynamics and Control of Process Systems (DYCOPS), Leuven, Belgium, 2010, pp. 320–325.
    30. J. Hasenauer et al., “Single-cells vs. cell populations - From a binary decision to a continuous  response,” in Proc. Conf. Systems Biology of Mammalian Cells (SBMC), Freiburg, Germany, 2010.
    31. A. Kramer, J. Hasenauer, F. Allgöwer, and N. Radde, “Computation of the posterior entropy in a Bayesian framework for  parameter estimation in biological networks,” in Proc. IEEE Int. Conf. Control Applications (CCA), Yokohama, Japan, 2010, pp. 493–498.
    32. A. Kramer and N. Radde, “Towards experimental design using a Bayesian framework for parameter  identification in dynamic intracellular network models,” in Procedia Comp. Sci., 2010, vol. 1, no. 1, pp. 1639--1647.
    33. M. Kögel, R. Blind, and F. Allgöwer, “Optimal Control Over Unreliable Networks with Uncertain Loss Rates,” in Proc. American Control Conf. (ACC), Baltimore, MD, USA, 2010, pp. 3672–3677.
    34. C. Maier, C. Böhm, F. Deroo, and F. Allgöwer, “Predictive control for polynomial systems subject to constraints  using sum of squares,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 3433--3438.
    35. M. A. Müller and A. D. Domínguez-García, “On Input-to-State Stability Notions for Reachability Analysis of  Power Systems,” in Proc. IEEE Int. Symp. Circuits and Systems (ISCAS), 2010.
    36. M. A. Müller and D. Liberzon, “State-norm estimators for switched nonlinear systems under average  dwell-time,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 1275–1280.
    37. M. A. Müller and D. Liberzon, “Input/output-to-state stability of switched nonlinear systems,” in Proc. American Control Conf. (ACC), Baltimore, Maryland, USA, 2010, pp. 1708–1712.
    38. M. Reble and F. Allgöwer, “Stabilizing design parameters for model predictive control of constrained  nonlinear time-delay systems,” in Proc. 9th IFAC Workshop on Time Delay Systems, Prague, Czech Republic, 2010.
    39. M. Reble and F. Allgöwer, “General Design Parameters of Model Predictive Control for Nonlinear  Time-Delay Systems,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 176--181.
    40. M. Reble and F. Allgöwer, “General Design Parameters of Model Predictive Control for Nonlinear Time-Delay Systems,” in Proc.\ 49th IEEE Conf.\ Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 176--181.
    41. D. Schlipf, S. Schuler, P. Grau, F. Allgöwer, and M. Kühn, “Look-Ahead Cyclic Pitch Control Using LIDAR,” in Proc. of the Science of Making Torque from Wind (TORQUE), 2010.
    42. G. S. Schmidt, C. Ebenbauer, and F. Allgöwer, “Synchronization Conditions for Lyapunov Oscillators,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 6230--6235.
    43. G. S. Schmidt, J. Wu, U. Münz, and F. Allgöwer, “Consensus in Bistable and Multistable Multi-Agent Systems,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 7135–7140.
    44. S. Schuler, W. Zhou, U. Münz, and F. Allgöwer, “Controller Structure Design for Decentralized Control of Higher Order  Subsystems,” in Proc. 2nd IFAC Workshop on Estimation and Control of Networked Systems  (NecSys), Annecy, France, 2010, pp. 296--274.
    45. S. Schuler, D. Schlipf, M. Kühn, and F. Allgöwer, “$\ell_1$-Optimal Multivariable Pitch Control for Load Reduction  on Large Wind Turbines,” in Proc. Scientific Track at the European Wind Energy Conf. (EWEC), Warsaw, Poland, 2010, pp. 110–112.
    46. S. Schuler, U. Münz, and F. Allgöwer, “Optimal Controller Structure Reduction for Decentralized Control,” in Proc. 4th IFAC Symp. System, Structure and Control (SSSC), Ancona, Italy, 2010, pp. 303–308.
    47. S. Waldherr, F. Allgöwer, and N. Radde, “Generic bifurcations in the dynamics of biochemical networks,” in Proc. IEEE Int. Conf. Control Applications (CCA), Yokohama, Japan, 2010, pp. 135--141.
    48. P. Wieland, G. S. Schmidt, R. Sepulchre, and F. Allgöwer, “Phase Synchronization through Entrainment by a Consensus Input,” in Proc. 49th IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, 2010, pp. 535--539.
    49. P. Wieland and F. Allgöwer, “On consensus among identical linear systems using input-decoupled  functional observers,” in Proc. American Control Conf. (ACC), Baltimore, MD, USA, 2010, pp. 1641–1646.
    50. S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “Robust model predictive control with disturbance invariant sets,” in Proc. American Control Conf. (ACC), Baltimore, MD, USA, 2010, pp. 6262--6267.
    51. S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “MPC with one free control action for constrained LPV systems,” in Proc. IEEE Int. Conf. Control Applications (CCA), Yokohama, Japan, 2010, pp. 1343–1348.
    52. D. Zelazo and M. Mesbahi, “$H_ınfty$ Performance and Robust Topology Design of  Relative Sensing Networks,” in Proc. American Control Conf. (ACC), Baltimore, Maryland, USA, 2010, pp. 4474–4479.
    53. D. Schittler, S. Waldherr, and F. Allgöwer, “Switch models for cell differentiation: Bifurcation analysis reveals  distinct switching properties.” 2010.
    54. D. Schittler, S. Waldherr, J. Hasenauer, and F. Allgöwer, “Modeling genetic switching in osteoblast cell fate driven by extrinsic  and intrinsic signals.” 2010.
    55. D. Schittler et al., “Model-based design of osteoblast differentiation stimuli.” 2010.
    56. D. Schittler, S. Waldherr, and F. Allgöwer, “Switch models for cell differentiation: Bifurcation analysis reveals distinct switching properties.” 2010.
    57. U. Münz, “Delay Robustness in Cooperative Control,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2010.
    58. T. Raff, “Impulsive Obervers for Continuous-Time Systems and Global Output  Feedback Control,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2010.
    59. P. Wieland, “From static to dynamic couplings in consensus and synchronization  among identical and non-identical systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2010.
  11. 2009

    1. N. S. Bar and N. Radde, “Long-term prediction of fish growth under varying ambient temperature  using a multiscale dynamic model,” BMC Sys. Biol., vol. 3, no. 1, p. 107, 2009.
    2. J. E. Cohen, D. Schittler, D. Raffaelli, and D. C. Reuman, “Food webs are more than the sum of their tri-trophic parts,” Proc. Natl. Acad. Sci. U. S. A., vol. 106, no. 52, pp. 22335–22340, 2009.
    3. F. Dörfler, J. K. Johnsen, and F. Allgöwer, “An introduction to interconnection and damping assignment passivity-based  control in process engineering,” J. Proc. Contr., vol. 19, no. 9, pp. 1413–1426, 2009.
    4. T. Eißing, M. Chaves, and F. Allgöwer, “Live and let die--A systems biology view on cell death,” Comp. & Chem. Eng., vol. 33, pp. 583--589, 2009.
    5. J. Gebert, N. Radde, U. Faigle, J. Strösser, and A. Burkovski, “Modeling and simulation of nitrogen regulation in Corynebacterium  glutamicum,” Discrete Appl. Math., 2009.
    6. M. Lang, S. Waldherr, and F. Allgöwer, “Amplitude Distribution of Stochastic Oscillations in Biochemical  Networks due to Intrinsic Noise,” PMC Biophysics, vol. 2, p. 10, 2009.
    7. D. Q. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control of constrained linear  systems: Time varying case,” Automatica, vol. 45, no. 9, pp. 2082--2087, 2009.
    8. U. Münz and P. J. Zufiria, “Diagnosis of unknown parametric faults in non-linear stochastic dynamical  systems,” Int. J. Control, vol. 82, no. 4, pp. 603–619, 2009.
    9. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Consensus reaching in multi-agent packet-switched networks with non-linear  coupling,” Int. J. Control, vol. 82, no. 5, pp. 953–969, 2009.
    10. U. Münz, C. Ebenbauer, T. Haag, and F. Allgöwer, “Stability Analysis of Time-Delay Systems with Incommensurate Delays  using Positive Polynomials,” IEEE Trans. Autom. Control, vol. 54, no. 5, pp. 1019–1024, 2009.
    11. N. Radde, “The impact of time-delays on the robustness of biological oscillators  and the effect of bifurcations on the inverse problem,” Eurasip Journal on Bioinformatics and Systems Biology, vol. 2009, 2009.
    12. N. Radde, N. S. Bar, and M. Banaji, “Graphical methods for analysing feedback in biological networks -  A survey -,” Int. J. Systems Science, 2009.
    13. T. Schweickhardt and F. Allgöwer, “On System Gains, Nonlinearity Measures, and Linear Models for Nonlinear  Systems,” IEEE Trans. Autom. Control, vol. 54, no. 1, pp. 62–78, 2009.
    14. S. Waldherr and F. Allgöwer, “Searching bifurcations in high-dimensional parameter space via a  feedback loop breaking approach,” Int. J. Systems Science, vol. 40, no. 7, pp. 769–782, 2009.
    15. J. Witt et al., “Mechanism of PP2A-mediated IKK$\beta$ dephosphorylation: a systems  biological approach,” BMC Sys. Biol., vol. 3, p. 71, 2009.
    16. L. Magni, D. M. Raimondo, and F. Allgöwer, Eds., Nonlinear Model Predictive Control -- Towards New Challenging Applications, vol. 384. Springer Berlin / Heidelberg, 2009.
    17. N. Radde and L. Kaderali, “A Bayes Regularized ODE Model for the Inference of Gene Regulatory  Networks,” S. Das, D. Caragea, W. H. Hsu, and S. M. Welch, Eds. IGI Global, 2009.
    18. C. Böhm, F. Heß, R. Findeisen, and F. Allgöwer, “An NMPC approach to avoid weakly observable trajectories,” in Nonlinear Model Predictive Control - Towards New Challenging Applications, vol. 384, L. Magni, D. Raimondo, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2009, pp. 275--284.
    19. C. Böhm, M. Merk, W. Fichter, and F. Allgöwer, “Spacecraft rate damping with predictive control using magnetic actuators  only,” in Nonlinear Model Predictive Control - Towards New Challenging Applications, vol. 384, L. Magni, D. Raimondo, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2009, pp. 511--520.
    20. C. Böhm, T. Raff, M. Reble, and F. Allgöwer, “LMI-based Model Predictive Control for Linear Discrete-Time Periodic  Systems,” in Nonlinear Model Predictive Control - Towards New Challenging Applications, vol. 384, L. Magni, D. Raimondo, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2009, pp. 99–108.
    21. C. Böhm, M. Merk, W. Fichter, and F. Allgöwer, “Spacecraft rate damping with predictive control using magnetic actuators only,” in Nonlinear Model Predictive Control - Towards New Challenging Applications, vol. 384, L. Magni, D. Raimondo, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2009, pp. 511--520.
    22. M. Chaves, T. Eißing, and F. Allgöwer, “Regulation of apoptosis via the NF$\kappa$B pathway: modeling and  analysis,” in Dynamics On and Of Complex Networks, N. Ganguly, A. Deutsch, and A. Mukherjee, Eds. Birkhäuser, 2009, pp. 19--34.
    23. C. Ebenbauer, T. Raff, and F. Allgöwer, “Dissipation inequalities in systems theory: An introduction and recent  results,” in 6th International Congress on Industrial and Applied Mathematics,  Z�rich, Switzerland, 16-20 July 2007, R. Jeltsch and G. Wanner, Eds. Zürich, Switzerland: European Mathematical Society Publishing House, 2009, pp. 23–42.
    24. L. Grüne, S. Sager, F. Allgöwer, H. G. Bock, and M. Diehl, “Vorausschauend planen, geziehlt handeln -- über die Regelung  und Steuerung technischer Prozesse,” in Produktionsfaktor Mathematik, M. Grötschel, K. Lucas, and V. Mehrmann, Eds. Springer Berlin / Heidelberg, 2009, pp. 27--62.
    25. B. Kern, C. Böhm, R. Findeisen, and F. Allgöwer, “Receding horizon control for linear periodic time-varying systems  subject to input constraints,” in Nonlinear Model Predictive Control - Towards New Challenging Applications, vol. 384, L. Magni, D. Raimondo, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2009, pp. 109--117.
    26. U. Münz, J. M. Rieber, and F. Allgöwer, “Robust Stabilization and $H_ınfty$ Control of Uncertain Distributed  Delay Systems,” in Topics in Time Delay Systems: Analysis, Algorithms, and Control, vol. 388, J. J. Loiseau, W. Michiels, S.-I. Niculescu, and R. Sipahi, Eds. Springer Berlin / Heidelberg, 2009, pp. 221–231.
    27. S. Streif, S. Waldherr, F. Allgöwer, and R. Findeisen, “Steady state sensitivity analysis of biochemical reaction networks.  A brief review and new methods,” in Systems Analysis of Biological Networks, A. Jayaraman and J. Hahn, Eds. Artech House, 2009, pp. 129--148.
    28. S. Yu, H. Chen, C. Böhm, and F. Allgöwer, “Enlarging the terminal region of NMPC with parameter-dependent  control law,” in Nonlinear Model Predictive Control - Towards New Challenging Applications, vol. 384, L. Magni, D. Raimondo, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2009, pp. 69--78.
    29. R. Blind and F. Allgöwer, “Estimating the Fates of the Control Packets for Networked Control  Systems with Loss of Control and Measurement Packets,” in Proc. 48th IEEE Conf. Decision and Control (CDC), 28th Chinese Control  Conf. (CCC), Shanghai, China, 2009, pp. 2687–1692.
    30. R. Blind and F. Allgöwer, “A controller design for Networked Control Systems with random delays  via the Jump Linear System approach, which reduces the effects of  the delay,” in Proc. European Control Conf. (ECC), Budapest, Hungary, 2009, pp. 1728–1733.
    31. R. Blind, S. Uhlich, B. Yang, and F. Allgöwer, “Robustification and Optimization of a Kalman Filter with Measurement  Loss using Linear Precoding,” in Proc. American Control Conf. (ACC), St. Louis, MO, USA, 2009, pp. 2222–2227.
    32. C. Breindl and F. Allgöwer, “Verification of multistability in gene regulation networks: A combinatorial  approach,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 5637–5642.
    33. C. Breindl, S. Waldherr, A. Hausser, and F. Allgöwer, “Modeling cofilin mediated regulation of cell migration as a biochemical  two-input switch,” in Proc. 3rd Foundations of Systems Biology in Engineering (FOSBE), 2009, pp. 60–63.
    34. C. Böhm, S. Yu, and F. Allgöwer, “Predictive control for constrained discrete-time periodic systems  using a time-varying terminal region,” in Proc. 14th Int. Conf. Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, 2009.
    35. C. Böhm, S. Yu, R. Findeisen, and F. Allgöwer, “Predictive control for Lure systems subject to constraints using  LMIs,” in Proc. European Control Conf. (ECC), Budapest, Hungary, 2009, pp. 3389--3394.
    36. M. Bürger and M. Guay, “A Backstepping Approach to Multivariable Robust Constraint Satisfaction  With Application to a VTOL Helicopter,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 5239–5244.
    37. C. Ebenbauer and A. Arsie, “On an Eigenflow Equation and its Structure Preserving Properties,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 7491–7496.
    38. C. Ebenbauer and A. Arsie, “Refining Lasalle’s invariance principle,” in Proc. American Control Conf. (ACC), St. Louis, Missouri, USA, 2009, pp. 108–112.
    39. R. M. Esfanjani, M. Reble, U. Münz, S. K. Y. Nikravesh, and F. Allgöwer, “Model Predictive Control of Constrained Nonlinear Time-Delay Systems,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 1324–1329.
    40. R. M. Esfanjani, M. Reble, U. Münz, S. K. Y. Nikravesh, and F. Allgöwer, “Model Predictive Control of Constrained Nonlinear Time-Delay Systems,” in Proc.\ 48th IEEE Conf.\ Decision and Control (CDC), Shanghai, China, 2009, pp. 1324–1329.
    41. T. Haag, U. Münz, and F. Allgöwer, “Comparison of Different Stability Conditions for Linear Time-Delay  Systems with Incommensurate Delays,” in Proc. 8th IFAC Workshop on Time Delay Systems, Sinaia, Romania, 2009, pp. 136–141.
    42. J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen, “Guaranteed steady-state bounds for uncertain chemical processes,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), 2009, pp. 674–679.
    43. A. Kramer and N. Radde, “A Stochastic Framework for Noise Separation in Dynamic Models of  Intracellular Networks,” in Proc. CASYS’09, 2009, no. 9, pp. 68–73.
    44. C. Maier and F. Allgöwer, “A Set-Valued Filter for Discrete Time Polynomial Systems using Sum  of Squares Programming,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 223--228.
    45. S. Maldonado, F. Allgöwer, and R. Findeisen, “Global Sensitivity Analysis of Force-induced Bone Growth and Adaptation  using Semidefinite Programming,” in Proc. 3rd Foundations of Systems Biology in Engineering (FOSBE), Denver, CO, USA, 2009, pp. 141–144.
    46. M. A. Müller, S. Waldherr, and F. Allgöwer, “The transcritical bifurcation in absolutely stable feedback systems,” in Proc. European Control Conf. (ECC), Budapest, Hungary, 2009, pp. 2146--2151.
    47. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Output Consensus Controller Design for Nonlinear Relative Degree  One Multi-Agent Systems with Delays,” in Proc. 8th IFAC Workshop on Time Delay Systems, Sinaia, Romania, 2009, pp. 370–375.
    48. U. Münz, C. Böhm, J. Eck, M. Reble, P. Schumm, and F. Allgöwer, “A Matlab-Based Game for Advanced Automatic Control Education,” in Proc. 8th IFAC Symp. Advances in Control Education, Kumamoto, Japan, 2009, pp. 140–145.
    49. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Generalized Nyquist Consensus Condition for Linear Multi-Agent  Systems with Heterogeneous Delays,” in Proc. 1st IFAC Workshop on Estimation and Control of Networked Systems  (NecSys), Venice, Italy, 2009, pp. 24–29.
    50. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Generalized Nyquist Consensus Condition for Large High-Order Linear  Multi-Agent Systems with Communication Delays,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 4765–4771.
    51. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Generalized Nyquist Consensus Condition for Large High-Order Linear Multi-Agent Systems with Communication Delays,” in Proc.\ 48th IEEE Conf.\ Decision and Control (CDC), Shanghai, China, 2009, pp. 4765–4771.
    52. N. Radde, N. S. Bar, and A. Tresch, “A comparison of likelihoods for dynamic stochastic models of biological  networks,” in WCSB Conference proceedings, 2009.
    53. M. Reble and F. Allgöwer, “Modellprädiktive Regelung für nichtlineare Totzeitsysteme,” in Tagungsband Workshop GMA-Fachausschuss 1.40 ``Theoretische Verfahren  der Regelungstechnik’’, 2009.
    54. M. Reble, C. Böhm, and F. Allgöwer, “Nonlinear Model Predictive Control for Periodic Systems using LMIs,” in Proc. European Control Conf. (ECC), Budapest, Hungary, 2009, pp. 3365–3370.
    55. G. S. Schmidt, U. Münz, and F. Allgöwer, “Multi-Agent Speed Consensus via Delayed Position Feedback with Application  to Kuramoto Oscillators,” in Proc. European Control Conf. (ECC), Budapest, Hungary, 2009, pp. 2464–2469.
    56. S. Schuler and F. Allgöwer, “$\ell_ınfty$-Gain Model Reduction for Discrete Time Systems  via LMIs,” in Proc. American Control Conf. (ACC), St. Louis, MO, USA, 2009, pp. 5701–5706.
    57. S. Waldherr, J. Hasenauer, and F. Allgöwer, “Estimation of biochemical network parameter distributions in cell  populations,” in Proc. 15th IFAC Symp. System Identification (SYSID), Brussels, Belgium, 2009, pp. 1265--1270.
    58. S. Waldherr, F. Allgöwer, and E. W. Jacobsen, “Kinetic perturbations as robustness analysis tool for biochemical  reaction networks,” in Proc. 48th IEEE Conf. Decision and Control (CDC), Shanghai, China, 2009, pp. 4572--4577.
    59. S. Waldherr, J. Hasenauer, and F. Allgöwer, “Estimation of biochemical network parameter distributions in cell populations,” in Proc.\ 15th IFAC Symp.\ System Identification (SYSID), Brussels, Belgium, 2009, pp. 1265--1270.
    60. S. Waldherr, F. Allgöwer, and E. W. Jacobsen, “Kinetic perturbations as robustness analysis tool for biochemical reaction networks,” in Proc.\ 48th IEEE Conf.\ Decision and Control (CDC), Shanghai, China, 2009, pp. 4572--4577.
    61. P. Wieland and F. Allgöwer, “An Internal Model Principle for Consensus in Heterogeneous Linear  Multi-Agent Systems,” in Proc. 1st IFAC Workshop on Estimation and Control of Networked Systems  (NecSys), Venice, Italy, 2009, pp. 7–12.
    62. P. Wieland and F. Allgöwer, “An Internal Model Principle for Synchronization,” in Proc. 7th IEEE Int. Conf. Control and Automation, Christchurch, New Zealand, 2009, pp. 285–290.
    63. S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “Moving horizon $\ell_2$ control of LPV systems subject to constraints,” in Proc. 14th Int. Conf. Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, 2009, pp. 354–359.
    64. S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “Stabilizing model predictive control for LPV systems subject to  constraints with parameter-dependent control law,” in Proc. American Control Conf. (ACC), St. Louis, 2009, pp. 3118--3123.
    65. S. Waldherr and F. Allgöwer, “A feedback approach to bifurcation analysis in biochemical networks  with many parameters.” Oct-2009.
    66. S. Waldherr and F. Allgöwer, “A feedback approach to bifurcation analysis in biochemical networks with many parameters.” Oct-2009.
    67. S. Waldherr, M. Doszczak, M. Schliemann, J. Schreiner, P. Scheurich, and F. Allgöwer, “The TNF Receptor Signalling Network: Modular Modelling and Cell-type  Specific Analysis.” 2009.
    68. J. Aßfalg, “Robust fault detection and isolation of nonlinear systems with augmented  state models,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2009.
    69. J. Maess, “Modeling and Control of Piezoelectric Tube Actuators and Dynamic  Atomic Force Microscopes,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2009.
    70. S. Waldherr, “Uncertainty and robustness analysis of biochemical reaction networks  via convex optimisation and robust control theory,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2009.
  12. 2008

    1. R. Blind, U. Münz, and F. Allgöwer, “Modeling, Analysis, and Design of Networked Control Systems using  Jump Linear Systems,” at-Automatisierungstechnik, vol. 56, no. 1, pp. 20–28, 2008.
    2. M. Chaves, T. Eißing, and F. Allgöwer, “Bistable Biological Systems: A Characterization Through Local Compact  Input-to-State Stability,” IEEE Trans. Autom. Control, vol. 53, no. Special Issue, pp. 87–100, 2008.
    3. C. Ebenbauer and A. Arsie, “On an eigenflow equation and its Lie algebraic generalization,” Communications in Information and Systems, vol. 8, no. 1, pp. 147–170, 2008.
    4. C. Ebenbauer and F. Allgöwer, “A Dissipation Inequality for the Minimum Phase Property,” IEEE Trans. Autom. Control, vol. 53, no. 3, pp. 821–826, 2008.
    5. C. Ebenbauer and A. Arsie, “On an eigenflow equation and its Lie algebraic generalization,” Communications in Information and Systems, vol. 8, no. 1, pp. 147–170, 2008.
    6. C. Ebenbauer and F. Allgöwer, “A Dissipation Inequality for the Minimum Phase Property,” IEEE Trans.\ Autom.\ Control, vol. 53, no. 3, pp. 821–826, 2008.
    7. J. Maess, A. J. Fleming, and F. Allgöwer, “Simulation of Dynamics-Coupling in Piezoelectric Tube Scanners by  Reduced Order Finite Element Analysis,” Review of Scientific Instruments, vol. 79, no. 1, pp. 1–9, 2008.
    8. S. Maldonado, R. Findeisen, and F. Allgöwer, “Describing force-induced bone groth and adaptation by a mathematical  model,” J. Musculoskel. Neuronal Interact., vol. 8, no. 1, pp. 15--17, 2008.
    9. S. Maldonado, R. Findeisen, and F. Allgöwer, “Understanding the process of force-induced bone growth and adaptation  through a mathematical model,” Bone, vol. 42, Supplement 1, p. S61, 2008.
    10. N. Radde, J. Gebert, U. Faigle, R. Schrader, and K. Schnetz, “Modeling feedback loops in the H-NS-mediated regulation of the Escherichia  coli bgl operon.,” Journal of Theoretical Biology, vol. 250, no. 2, 2008.
    11. N. Radde and L. Kaderali, “Inference of an oscillatory model for the yeast cell cycle,” Discrete Appl. Math., vol. doi:10.1016/j.dam.2008.06.036, 2008.
    12. N. Radde, “The effect of time scale differences and time-delays on the structural  stability of oscillations in a two-gene network,” Adv. Complex. Syst., vol. 11, no. 3, pp. 471–483, 2008.
    13. S. Waldherr, T. Eißing, and F. Allgöwer, “Rückkopplungen im Leben und Sterben einer Zelle: Ansätze  zur systemtheoretischen Analyse,” at-Automatisierungstechnik, vol. 56, pp. 233--240, 2008.
    14. S. Waldherr and M. Zeitz, “Conditions for the existence of a flat input,” Int. J. Control, vol. 81, no. 3, pp. 439--443, 2008.
    15. S. Waldherr and M. Zeitz, “Conditions for the existence of a flat input,” Int.\ J.\ Control, vol. 81, no. 3, pp. 439--443, 2008.
    16. K. Yao, F. G., and F. Allgöwer, “Barrel temperature control during operation transition in injection  molding,” Control Engineering Practice, vol. 16, pp. 1259–1264, 2008.
    17. C. Maier, T. Haag, U. Münz, and F. Allgöwer, “Construction of quadratic Lyapunov-Krasovskii functionals for linear  time delay systems with multiple uncertain delays,” in Mathematical Problems in Engineering and Aerospace Sciences: ICNPAA  2008, vol. 5, S. Sivasundaram, Ed. Cambridge, UK: Cambridge Scientific Publisher Ltd, 2008.
    18. J.-S. Kim and F. Allgöwer, “Nonlinear Synchronization of Coupled Oscillators: The Polynomial  Case,” in Analysis and Design of Nonlinear Control Systems, In Honor of Alberto  Isidori, A. Astolfi and L. Marconi, Eds. Springer Berlin / Heidelberg, 2008, pp. 339–351.
    19. C. Böhm, T. Raff, R. Findeisen, and F. Allgöwer, “Calculating the terminal region of NMPC for Lure systems,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2008, pp. 1127--1132.
    20. C. Böhm, R. Findeisen, and F. Allgöwer, “Avoidance of poorly observable trajectories: A predictive control  perspective,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 1952--1957.
    21. M. Bürger, T. Raff, C. Ebenbauer, and F. Allgöwer, “Extensions on a Certainty-Equivalence Feedback Design with a Class  of Feedbacks Which Guarantee ISS,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2008, pp. 383–388.
    22. N. Dmitruk, R. Findeisen, and F. Allgöwer, “Optimal measurement feedback control of finite-time continous linear  systems,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 15339–15344.
    23. D. Geffen, R. Findeisen, M. Schliemann, F. Allgöwer, and M. Guay, “Observability based parameter identifiability for biochemical reaction  networks,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2008, pp. 2130–2135.
    24. J. Hasenauer, S. Waldherr, and F. Allgöwer, “Global sensitivity analysis of biochemical reaction networks using  semidefinite programming,” in Proc. 9th Int. Conf. Systems Biology (ICSB), Gothenburg, Sweden, 2008.
    25. J. K. Johnsen, F. Dörfler, and F. Allgöwer, “$L_2$-gain of Port-Hamiltonian systems and application  to a biochemical fermenter model,” in Proc. American Control Conf. (ACC), Seattle, USA, 2008, pp. 153–158.
    26. J. Maess, J. Becker, L. Gaul, and F. Allgöwer, “Two-Degree-of-Freedom Tracking Control of Piezoelectric Tube Scanners  in Two-Dimensional Scanning Applications,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 8257–8262.
    27. J. Maess, A. J. Fleming, and F. Allgöwer, “Model-Based Vibration Suppression in Piezoelectric Tube Scanners  through Induced Voltage Feedback,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2008, pp. 2022–2027.
    28. U. Münz, P. Schumm, and F. Allgöwer, “Educational Games in Control,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 12625–12630.
    29. U. Münz, J. M. Rieber, and F. Allgöwer, “Robust stability of Distributed Delay Systems,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 12354–12358.
    30. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Delay-Dependent Rendezvous and Flocking of Large Scale Multi-Agent  Systems with Communication Delays,” in Proc. 47th IEEE Conf. Decision and Control (CDC), Cancun, Mexico, 2008, pp. 2038–2043.
    31. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Nonlinear Multi-Agent System Consensus with Time-Varying Delays,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 1522–1527.
    32. T. Raff, D. Sinz, and F. Allgöwer, “Model Predictive Control of Uncertain Continuous-Time Systems with  Piecewise Constant Control Input: A Convex Approach,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2008, pp. 1109–1114.
    33. T. Raff, M. Kögel, and F. Allgöwer, “Observer with Sample-and-Hold Updating for Lipschitz Nonlinear Systems  with Nonuniformly Sampled Measurements,” in Proc. American Control Conf. (ACC), Seattle, WA, USA, 2008, pp. 5254–5257.
    34. T. Raff and F. Allgöwer, “An Observer that Converges in Finite Time Due to Measurement-based  State Updates,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 2693–2695.
    35. K. Schmidt and C. Breindl, “On maximal permissiveness of hierarchical and modular supervisory  control approaches for discrete event systems,” in Proc. 9th Int. Workshop Discrete Event Syst. (WODES), 2008, pp. 462–467.
    36. S. Waldherr, T. Eißing, and F. Allgöwer, “Analysis of Feedback Mechanisms in Cell-biological Systems,” in Proc. of the 17th IFAC World Congress, Seoul, Korea, 2008, pp. 15861--15866.
    37. S. Waldherr, R. Findeisen, and F. Allgöwer, “Global Sensitivity Analysis of Biochemical Reaction Networks via  Semidefinite Programming,” in Proc. of the 17th IFAC World Congress, Seoul, Korea, 2008, pp. 9701–9706.
    38. S. Waldherr, M. Doszczak, M. Schliemann, J. Schreiner, P. Scheurich, and F. Allgöwer, “The TNF Receptor Signalling Network: Modular Modelling and Cell-type  Specific Analysis,” in 2nd Conference on Systems Biology of the Mammalian Cell, Dresden, 2008.
    39. S. Waldherr, J. Hasenauer, and F. Allgöwer, “Global sensitivity analysis of uncertain biochemical reaction networks,” in 2nd Int. Work. Syst. Biol., 2008.
    40. P. Wieland, J.-S. Kim, H. Scheu, and F. Allgöwer, “On consensus in multi-agent systems with linear high-order agents,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 1541–1546.
    41. S. Yu, H. Chen, C. Böhm, and F. Allgöwer, “Moving horizon $H_ınfty$ control based on T-S models,” in Proc. Int. Workshop on Assessment and Future Directions of Nonlinear  Model Predictive Control, Pavia, Italy, 2008.
    42. S. Yu, H. Chen, C. Böhm, and F. Allgöwer, “Moving horizon $H_ınfty$ control based on T-S models,” in Proc.\ Int.\ Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control, Pavia, Italy, 2008.
    43. R. Blind, U. Münz, and F. Allgöwer, “Modeling, Analysis, and Stabilization of Networked Control Systems:  A Jump Linear Systems Approach.” 2008.
    44. S. Maldonado, R. Findeisen, and F. Allgöwer, “Understanding the process of force-induced bone growth and adaptation  by a mathematical model.” 2008.
    45. S. Maldonado, R. Findeisen, and F. Allgöwer, “Towards Optimal Treatment Design: Model predicts the synergistic  effects of PGE2 and mechanical loading during bone remodeling.” 2008.
    46. S. Maldonado, R. Findeisen, and F. Allgöwer, “Towards Optimal Treatment Design:  Model predicts the synergistic effects of  PGE2 and mechanical loading during  bone remodeling.” 2008.
    47. M. Ahdesmäki et al., Eds., Proc.\ 5th Int. Workshop on Comput.\ Syst.\ Biol.\ (WCSB08). 2008.
    48. M. Ahdesmäki et al., Eds., Proceedings of WCSB08. 2008.
  13. 2007

    1. C. Ebenbauer and F. Allgöwer, “Stability Analysis of Constrained Control Systems: An Alternative  Approach,” Syst. Contr. Lett., vol. 56, no. 2, pp. 93–98, 2007.
    2. C. Ebenbauer, T. Raff, and F. Allgöwer, “Certainty-Equivalence Feedback Design with Polynomial-Type Feedbacks  Which Guarantee ISS,” IEEE Trans. Autom. Control, vol. 52, no. 4, pp. 716–720, 2007.
    3. T. Eißing, S. Waldherr, F. Allgöwer, P. Scheurich, and E. Bullinger, “Response to Bistability in Apoptosis: Roles of Bax, Bcl-2,  and Mitochondrial Permeability Transition Pores,” Biophysical J., vol. 92, no. 9, pp. 3332--3334, 2007.
    4. T. Eißing, S. Waldherr, F. Allgöwer, P. Scheurich, and E. Bullinger, “Steady state and (bi-) stability evaluation of simple protease  signalling networks,” BioSystems, vol. 90, no. 3, pp. 591–601, 2007.
    5. J. Gebert, N. Radde, and G.-W. Weber, “Modeling gene regulatory networks with piecewise linear differential  equations,” European Journal of Operational Research, vol. 181, no. 3, pp. 1148–1165, 2007.
    6. M. Journée, T. Schweickhardt, and F. Allgöwer, “Comparative assessment of old and new suboptimal control schemes  on three example processes,” Int. J. of Tomography & Statistics, vol. 6, no. S07, pp. 45–50, 2007.
    7. J.-S. Kim, T.-W. Yoon, and C. De Persis, “Discrete-time supervisory control of input-constrained neutrally  stable linear systems via state-dependent dwell-time switching,” Syst. Contr. Lett., vol. 56, pp. 484–492, 2007.
    8. R. Lepore, A. Vande Wouwer, M. Remy, R. Findeisen, Z. K. Nagy, and F. Allgöwer, “Optimization strategies for a MMA polymerization reactor,” Comp. & Chem. Eng., vol. 31, no. 4, pp. 281–291, 2007.
    9. U. Münz, P. Schumm, A. Wiesebrock, and F. Allgöwer, “Motivation and Learning Progress through Educational Games,” IEEE Trans. Industrial Electronics, vol. 54, no. 6, pp. 3141–3144, 2007.
    10. Z. Nagy and F. Allgöwer, “A nonlinear model predictive control approach for robust end-point  property control of a thin-film deposition process,” Int. J. Robust and Nonlinear Control, vol. 17, no. 17, pp. 1600–1613, 2007.
    11. Z. Nagy, B. Mahn, R. Franke, and F. Allgöwer, “Evaluation study of an efficient output feedback nonlinear model  predictive control for temperature tracking in an industrial batch  reactor,” Control Engineering Practice, vol. 15, no. 7, pp. 839–850, 2007.
    12. T. Schweickhardt and F. Allgöwer, “Linear control of nonlinear systems based on nonlinearity measures,” J. Proc. Contr., vol. 17, no. 3, pp. 273–284, 2007.
    13. T. Schweickhardt and F. Allgöwer, “Linear control of nonlinear systems based on nonlinearity measures,” J.\ Proc.\ Contr., vol. 17, no. 3, pp. 273–284, 2007.
    14. T.-W. Yoon, J.-S. Kim, and A. S. Morse, “Supervisory Control using a New Control-relevant Switching,” Automatica, vol. 43, pp. 1791--1798, 2007.
    15. W. Zhang, J. M. Rieber, and D. Gu, “Optimal dead-time compensator for stable and integrating processes  with time delay,” J. Proc. Contr., vol. 18, pp. 449–457, 2007.
    16. F. Allgöwer, L. del Re, M. Diehl, and R. Scattolini, Eds., Predictive Control of Combustion Engines. Trauner Verlag, 2007.
    17. R. Findeisen, L. B. Biegler, and F. Allgöwer, Eds., Assessment and Future Directions of Nonlinear Model Predictive Control, no. 358. Springer Berlin / Heidelberg, 2007.
    18. L. Kaderali and N. Radde, “Inferring gene regulatory networks from expression data,” in Studies in Computational Intelligence, vol. 1, Springer, 2007.
    19. H. Chen, X. Gao, H. Wang, and R. Findeisen, “On disturbance attenuation of nonlinear moving horizon control,” in Assessment and Future Directions of Nonlinear Model Predictive Control, vol. 358, R. Findeisen, L. Biegler, and F. Allgöwer, Eds. Springer Berlin / Heidelberg, 2007, pp. 283–294.
    20. M. Diehl, R. Findeisen, and F. Allgöwer, “A Stabilizing Real-time Implementation of Nonlinear Model Predictive  Control,” in Real-Time PDE-Constrained Optimization, L. Biegler, O. Ghattas, M. Heinkenschloss, D. Keyes, and B. van Bloem Wanders, Eds. Philadephia, PA, USA: Society for Industrial and Applied Mathematics, 2007, pp. 23–52.
    21. C. Ebenbauer and F. Allgöwer, “A Dissipation Inequality for the Minimum Phase Property of Nonlinear  Control Systems,” in Advances in Control Theory and Applications, vol. 353, C. Bonivento, L. Marconi, C. Rossi, and A. Isidori, Eds. Springer Berlin / Heidelberg, 2007, pp. 71–83.
    22. T. Eißing, S. Waldherr, and F. Allgöwer, “Modelling and Analysis of Cell Death Signalling,” in Biology and Control Theory: Current Challenges, vol. 357, I. Queinnec, S. Tarbouriech, G. Garcia, and S.-I. Niculescu, Eds. Springer Berlin / Heidelberg, 2007, pp. 161--180.
    23. J. Johnsen and F. Allgöwer, “Interconnection and Damping Assignment Passivity-Based Control of  a Four-Tank System,” in Lagrangian and Hamiltonian Methods for Nonlinear Control 2006, vol. 366, F. Bullo and K. Fujimoto, Eds. Springer Berlin / Heidelberg, 2007, pp. 111–122.
    24. R. Blind, U. Münz, and F. Allgöwer, “Almost Sure Stability and Transient Behavior of Stochastic Nonlinear  Jump Systems Motivated by Networked Control Systems,” in Proc. 46th IEEE Conf. Decision and Control (CDC), New Orleans, LA, USA, 2007, pp. 3327–3332.
    25. C. Ebenbauer, “Detecting oscillatory behavior using Lyapunov functions,” in Proc. 46th IEEE Conf. Decision and Control (CDC), New Orleans, LA, USA, 2007, pp. 1615–1620.
    26. C. Ebenbauer, “A dynamical system that computes eigenvalues and diagonalizes matrices  with a real spectrum,” in Proc. 46th IEEE Conf. Decision and Control (CDC), New Orleans, LA, USA, 2007, pp. 1704–1709.
    27. R. Findeisen, J. Sjoberg, and F. Allgöwer, “Model predictive control of continuous time nonlinear differential  algebraic systems,” in Proc. 7th IFAC Symp. Nonlinear Control Systems (NOLCOS), Pretoria, South Africa, 2007, pp. 165–171.
    28. R. Findeisen, T. Raff, and F. Allgöwer, “Sampled-Data Nonlinear Model Predictive Control for Constrained Continuous  Time Systems,” in Advanced Strategies in Control Systems with Input and Output Constraints, 2007, vol. 346, pp. 207–235.
    29. D. Geffen, R. Findeisen, M. Schliemann, F. Allgöwer, and M. Guay, “The question of parameter identifiability for biochemical reaction  networks considering the NF-$\kappa$B signal transduction pathway,” in Proc. 2nd Foundations of Systems Biology in Engineering (FOSBE), Stuttgart, Germany, 2007, pp. 509–514.
    30. J.-S. Kim and F. Allgöwer, “Nonlinear Observer-based Synchronization of Neuron Models,” in 3rd International IEEE Scientific Conference on Physics and Control, Potsdam, Germany, 2007.
    31. J.-S. Kim and F. Allgöwer, “A nonlinear synchronization scheme for polynomial systems,” in Proc. American Control Conf. (ACC), New York City, NY, USA, 2007, pp. 2588–2593.
    32. J. Maess and F. Allgöwer, “Closed-Loop Simulation of Kelvin Probe Force Microscopy based on  Reduced Finite Element Cantilever Modeling,” in 3rd International IEEE Scientific Conference on Physics and Control, Potsdam, Germany, 2007.
    33. J. Maess, A. J. Fleming, and F. Allgöwer, “Simulation of Piezoelectric Tube Actuators by Reduced Finite Element  Models for Controller Design,” in Proc. American Control Conf. (ACC), New York City, NY, USA, 2007, pp. 4221–4226.
    34. S. Maldonado, R. Findeisen, and F. Allgöwer, “Phenomenological Mathematical Modeling and Analysis of Force-induced  Bone Growth and Adaptation,” in Proc. 2nd Foundations of Systems Biology in Engineering (FOSBE), Stuttgart, Germany, 2007, pp. 147--152.
    35. U. Münz, C. Ebenbauer, and F. Allgöwer, “Stability of Networked Systems with Multiple Delays Using Linear  Programming,” in Proc. American Control Conf. (ACC), New York City, NY, USA, 2007, pp. 5515--5520.
    36. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Multi-Agent System Consensus in Packet-Switched Networks,” in Proc. European Control Conf. (ECC), Kos, Greece, 2007, pp. 4598--4603.
    37. U. Münz and F. Allgöwer, “$L_2$-Gain Based Controller Design for Linear Systems  with Distributed Delays and Rational Delay Kernels,” in Proc. 7th IFAC Symp. Time-Delay Systems, Nantes, France, 2007.
    38. N. Radde and L. Kaderali, “Bayesian inference of gene regulatory networks using gene expression  time series data,” in Bioinformatics Research and Development, BIRD07, 2007, vol. 4414.
    39. T. Raff, C. Angrick, R. Findeisen, J.-S. Kim, and F. Allgöwer, “Model Predictive Control for Nonlinear Time-Delay Systems,” in Proc. 7th IFAC Symp. Nonlinear Control Systems (NOLCOS), Pretoria, South Africa, 2007, pp. 134–139.
    40. T. Raff and F. Allgöwer, “Observers with Impulsive Dynamical Behavior for Linear and Nonlinear  Continuous-Time Systems,” in Proc. 46th IEEE Conf. Decision and Control (CDC), New Orleans, LA, USA, 2007, pp. 4287–4292.
    41. T. Raff and F. Allgöwer, “An Impulsive Observer that Estimates the Exact State of a Linear  Continuous-Time System in Predetermined Finite Time,” in Proc. 12th Mediterranean Conf. Control and Automation (MED), Athens, Greece, 2007.
    42. T. Raff and F. Allgöwer, “Observer Design via Absolute Stability for a Class of Nonlinear Descriptor  Systems,” in Proc. 7th IFAC Symp. Nonlinear Control Systems (NOLCOS), Pretoria, South Africa, 2007, pp. 307–312.
    43. M. Reble, U. Münz, and F. Allgöwer, “Diagnosis of Parametric Faults in Multivariable Nonlinear Systems,” in Proc. 46th IEEE Conf. Decision and Control (CDC), New Orleans, LA, USA, 2007, pp. 366–371.
    44. A. Schöllig, U. Münz, and F. Allgöwer, “Topology-Dependent Stability of a Network of Dynamical Systems with  Communication Delays,” in Proc. European Control Conf. (ECC), Kos, Greece, 2007, pp. 1197--1202.
    45. S. Waldherr and F. Allgöwer, “A feedback approach to bifurcation analysis in biochemical networks  with many parameters,” in Proc. 2nd Foundations of Systems Biology in Engineering (FOSBE), Stuttgart, Germany, 2007, pp. 479--484.
    46. S. Waldherr, T. Eißing, and F. Allgöwer, “Analysing biological feedback with tools from control theory,” in FEBS Advanced Lecture Course on Systems Biology, 2007.
    47. S. Waldherr, T. Eißing, M. Chaves, and F. Allgöwer, “Bistability preserving model reduction in apoptosis,” in Proc. 10th Int. IFAC Symp. Computer Appications in Biotechnology, Cancun, Mexico, 2007, pp. 327--332.
    48. S. Waldherr, T. Eißing, M. Chaves, and F. Allgöwer, “Bistability preserving model reduction in apoptosis,” in Proc.\ 10th Int.\ IFAC Symp.\ Computer Appications in Biotechnology, Cancun, Mexico, 2007, pp. 327--332.
    49. P. Wieland and F. Allgöwer, “Constructive Safety using Control Barrier Functions,” in Proc. 7th IFAC Symp. Nonlinear Control Systems (NOLCOS), Pretoria, South Africa, 2007, pp. 473--478.
    50. P. Wieland, C. Ebenbauer, and F. Allgöwer, “Ensuring Task-Independent Safety for Multi-Agent Systems by Feedback,” in Proc. American Control Conf. (ACC), New York City, NY, USA, 2007, pp. 3880–3885.
    51. K. L. Knierim, J. Maess, M. Schneider, and F. Allgöwer, “Regelung eines Achterbahn-Kettenliftes.” 2007.
    52. S. Maldonado, R. Findeisen, and F. Allgöwer, “Mathematical modeling of force induced bone growth and adaptation.” 2007.
    53. T. Eißing, “A systems science view on cell death signalling,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2007.
    54. J. M. Rieber, “Control of Uncertain Systems with l1 and Quadratic Performance Objectives,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2007.
  14. 2006

    1. R. Bars et al., “Theory, algorithms and technology in the design of control systems,” Annual Reviews in Control, vol. 30, pp. 19–30, 2006.
    2. M. Chaves, E. D. Sontag, and R. Albert, “Methods of robustness analysis for Boolean models of gene control  networks,” IEE Systems Biology, vol. 153, no. 4, pp. 154–167, 2006.
    3. C. Ebenbauer and F. Allgöwer, “Analysis and design of polynomial control systems using dissipation  inequalities and sum of squares,” Comp. & Chem. Eng., vol. 30, pp. 1601–1614, 2006.
    4. D. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control of constrained linear  systems,” Automatica, vol. 42, no. 7, pp. 1217–1222, 2006.
    5. D. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control of constrained linear systems,” Automatica, vol. 42, no. 7, pp. 1217–1222, 2006.
    6. N. Radde, J. Gebert, and C. V. Forst, “Systematic component selection for gene-network refinement,” Bioinformatics, vol. 22, no. 21, pp. 2674–2680, 2006.
    7. J. M. Rieber and F. Allgöwer, “From $H_ınfty$ control to multiobjective control:  an overview,” at-Automatisierungstechnik, vol. 54, no. 9, pp. 437--449, 2006.
    8. T. Schweickhardt and F. Allgöwer, “A robustness approach to linear control of mildly nonlinear processes,” Int. J. Robust and Nonlinear Control, vol. 17, no. 13, pp. 1163–1182, 2006.
    9. R. Findeisen, Nonlinear Model Predictive Control: A Sampled-Data Feedback Perspective. Düsseldorf: Fortschr.-Ber. VDI Reihe 8 Nr. 1087, VDI Verlag, 2006.
    10. J. Aßfalg and F. Allgöwer, “Fault diagnosis of constrained nonlinear systems using structured  augmented state models,” in Proc. IFAC SAFEPROCESS, Beijing, China, 2006, pp. 1375–1380.
    11. J. Aßfalg and F. Allgöwer, “Fault diagnosis with structured augmented state models: Modeling,  analysis, and design,” in Proc. 45th IEEE Conf. Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 1165–1170.
    12. J. Aßfalg, F. Allgöwer, and M. Fritz, “Constrained derivative-free augmented state estimation for a diesel  engine air path,” in Proc. 14th IFAC Symp. System Identification (SYSID), Newcastle, Australia, 2006, pp. 1382–1387.
    13. J. Aßfalg and F. Allgöwer, “Fault diagnosis with structured augmented state models: Modeling, analysis, and design,” in Proc.\ 45th IEEE Conf.\ Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 1165–1170.
    14. E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead, “System and control theory allows to further understanding of biological  signal transduction,” 2006.
    15. E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead, “System and control theory allows to further understanding of biological signal transduction,” 2006.
    16. M. Chaves, T. Eißing, and F. Allgöwer, “Identifying mechanisms for bistability in an apoptosis network,” in Réseaux d’Interactions: Analyse, Modélisation et Simulation  (RIAMS’06), Lyon, France, 2006.
    17. M. Chaves, E. D. Sontag, and R. Albert, “Structure and timescale analysis in genetic regulatory networks,” in Proc. 45th IEEE Conf. Decision and Control (CDC), 2006, pp. 2358–2363.
    18. M. Chaves, “Stability of rate-controlled zero-deficiency networks,” in Proc. 45th IEEE Conf. Decision and Control (CDC), 2006, pp. 5766–5771.
    19. C. Ebenbauer and F. Allgöwer, “Polynomial Control Systems: Analysis and Design via Dissipation Inequalities,” in Proc. of the 7th Chemical Process Control Conference (CPC), Lake  Lousie, Canada, 2006.
    20. C. Ebenbauer and F. Allgöwer, “Stability analysis for time-delay systems using Rekasius’s substitution  and sum of squares,” in Proc. 45th IEEE Conf. Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 5376--5381.
    21. C. Ebenbauer and F. Allgöwer, “Polynomial Control Systems:  Analysis and Design via Dissipation Inequalities,” in Proc. of the 7th Chemical Process Control Conference (CPC), Lake Lousie, Canada, 2006.
    22. T. Eißing et al., “Mathematical modeling of TNF induced apoptotic and anti-apoptotic  crosstalk in mammalian cells,” in Conference on Systems Biology of Mammalian Cells (SBMC), 2006, p. 66.
    23. T. Eißing et al., “Sensitivity analysis of programmed cell death and implications for  crosstalk phenomena during Tumor Necrosis Factor stimulation,” in Proc. IEEE Int. Conf. Control Applications (CCA), Munich, Germany, 2006, pp. 1746–1752.
    24. T. Eißing, F. Allgöwer, P. Scheurich, and E. Bullinger, “Bistability in cell signalling and applications to apoptosis -  principles and robustness aspects,” in Proceedings of the Hamilton Institute International Workshop on  Systems Biology, NUI Maynooth, Ireland, 2006, p. 39.
    25. T. Eißing, S. Waldherr, F. Allgöwer, and E. Bullinger, “Modelling and Analysis of Death and Survival Signalling:  Achievements and Trends,” in Workshop CNRS-NSF - Biology and control theory: current challenges, Toulouse, France, 2006.
    26. M. Farina, R. Findeisen, E. Bullinger, S. Bittanti, F. Allgöwer, and P. Wellstead, “Results towards Identifiability Properties of Biochemical Reaction  Networks,” in Proc. 45th IEEE Conf. Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 2104–2109.
    27. M. Farina, R. Findeisen, E. Bullinger, S. Bittanti, F. Allgöwer, and P. Wellstead, “Results towards Identifiability Properties of Biochemical Reaction Networks,” in Proc.\ 45th IEEE Conf.\ Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 2104–2109.
    28. J. Gebert and N. Radde, “A new approach for modeling prokaryotic biochemical networks with  differential equations,” in AIP Conference Proceedings of 7th International Conference on Computing  Anticipatory Systems (CASYS05), 2006, vol. 839.
    29. M. Herceg, T. Raff, R. Findeisen, and F. Allgöwer, “Nonlinear Model Predictive Control of a Turbocharged Diesel Engine,” in Proc. IEEE Int. Conf. Control Applications (CCA), Munich, Germany, 2006, pp. 2766–2771.
    30. M. Journée, T. Schweickhardt, and F. Allgöwer, “Comparative assessment of old and new suboptimal control schemes  on three example processes,” in Proc. 13th IFAC Workshop on Control Applications of Optimization, Paris-Cachan, France, 2006, pp. 189–194.
    31. R. Lepore, A. Vande Wouwer, M. Remy, R. Findeisen, Z. K. Nagy, and F. Allgöwer, “Scheduled optimization of an MMA polymerization process,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), Gramado, Brazil, 2006, pp. 695--703.
    32. S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer, “Modeling bone adaptation and remodeling initiated by mechanical stimuli,” in Proc. Int. Mediterranean Modelling Conf., 2nd European Modeling and  Simulation Symp. (EMSS), Barcelona, Spain, 2006, pp. 403--409.
    33. S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer, “Mathematical Modeling and Analysis of Force Induced Bone Growth,” in Proc. 28th Annual Int. Conf. IEEE Engineering in Medicine and Biology  Society (EMBC), New York, NY, 2006, pp. 3154--3157.
    34. D. Mayne, S. V. Raković, R. Findeisen, and F. Allgöwer, “Robust output feedback model predictive control for constrained linear  systems under uncertainty based on feed forward and positive invariant  feedback control,” in Proc. 45th IEEE Conf. Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 6618–6623.
    35. T. Raff, S. Huber, Z. K. Nagy, and F. Allgöwer, “Nonlinear Model Predictive Control of a Four Tank System: An Experimental  Stability Study,” in Proc. IEEE Int. Conf. Control Applications (CCA), Munich, Germany, 2006, pp. 237–242.
    36. T. Raff and F. Allgöwer, “An EKF-based Observer for Nonlinear Time-Delay Systems,” in Proc. American Control Conf. (ACC), Minneapolis, MN, USA, 2006, pp. 3130–3133.
    37. T. Raff, F. Lachner, and F. Allgöwer, “A Finite Time Unknown Input Observer for Linear Systems,” in Proc. 11th Mediterranean Conf. Control and Automation (MED), Ancona, Italy, 2006.
    38. J. M. Rieber, C. W. Scherer, and F. Allgöwer, “Robust $\ell_1$ performance analysis in face of parametric uncertainties,” in Proc. 45th IEEE Conf. Decision and Control (CDC), San Diego, CA, USA, 2006, pp. 5826--5831.
    39. J. M. Rieber, C. W. Scherer, and F. Allgöwer, “On complexity issues in multiobjective controller design using  convex optimization,” in Proc. 5th IFAC Symp. Robust Control Design, Toulouse, France, 2006.
    40. J. M. Rieber and F. Allgöwer, “Gain-scheduling in the $\ell_1$ framework: a flight control example,” in Proc. 5th IFAC Symp. Robust Control Design, Toulouse, France, 2006.
    41. T. Schweickhardt, P. Schumm, U. Münz, and F. Allgöwer, “Integration of E-Learning Modules in Automatic Control Education,” in Proc. 7th IFAC Symp. Advances in Control Education, Madrid, Spain, 2006.
    42. T. Schweickhardt and F. Allgöwer, “Good or bad -- when is plant nonlinearity an obstacle for control?,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), Gramado, Brazil, 2006, pp. 37–44.
    43. T. Schweickhardt and F. Allgöwer, “An approach to linear control of nonlinear processes,” in Proc. 16th European Symp. Computer Aided Process Engineering (ESCAPE),  9th Int. Symp. Process Systems Engineering (PSE), Garmisch-Partenkirchen, Germany, 2006, pp. 1299–1304.
    44. T. Schweickhardt and F. Allgöwer, “An approach to linear control of nonlinear processes,” in Proc.\ 16th European Symp.\ Computer Aided Process Engineering (ESCAPE), 9th Int.\ Symp.\  Process Systems Engineering (PSE), Garmisch-Partenkirchen, Germany, 2006, pp. 1299–1304.
    45. H. Shim, J. Lee, J.-S. Kim, and J. Back, “Output Regulation Problem and Solution for LTV Minimum Phase Systems  with Time-varying Exosystem,” in SICE-ICASE International Joint Conference 2006, 2006.
    46. S. Streif, R. Findeisen, and E. Bullinger, “Relating Cross Grammians and Sensitivity Ananlysis in Systems Biology,” in Proc. 17th Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Kyoto, 2006, pp. 437–442.
    47. S. Waldherr and F. Allgöwer, “Hopf bifurcations and feedback gain in signaling pathways,” in Conference on Systems Biology of Mammalian Cells, Heidelberg, Germany, 2006.
    48. S. Waldherr, T. Eißing, M. Chaves, and F. Allgöwer, “Preservation of bistability in the reduction of an apoptosis model,” in Genomes To Systems Conference, Manchester, UK, 2006.
    49. P. Wieland, T. Meurer, K. Graichen, and M. Zeitz, “Feedforward control design under input constraints for a tubular  reactor model,” in Proc. 45th IEEE Conf. Decision and Control (CDC), 2006, pp. 3968–3973.
    50. I. Alvarado, R. Findeisen, P. Kühl, D. Limón, and F. Allgöwer, “Iteratively Improving Moving Horizon Observers for Repetitive Processes.” 2006.
    51. S. Borchers, S. Maldonado, R. Findeisen, and F. Allgöwer, “Modeling the bone remodeling cycle due to mechanical force.” 2006.
    52. S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer, “Modeling and Analysis of Force Induced Bone Growth.” 2006.
    53. T. Schweickhardt, “Nonlinearity Assessment and Linear Control of Nonlinear Systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2006.
    54. T. Eißing, “Simplicity and Complexity in Modeling and Analysis of Biological  Signal Transduction: About and Beyond Cell Death,” 2006.
    55. T. Eißing, “Apoptosis Signaling: Mathematical Modeling and Analysis  as a Bistable System,” 2006.
    56. S. Waldherr, “Negative feedback loops in biochemical networks,” 2006.
    57. S. Waldherr, “Negative feedback loops in biochemical networks,” 2006.
  15. 2005

    1. F. Allgöwer, “Editorial: Nonlinear Model Predictive Control,” IEE Control Theory Appl., vol. 152, no. 3, pp. 257–258, 2005.
    2. E. Bullinger and F. Allgöwer, “Adaptive $łambda$-tracking for nonlinear higher relative degree  systems,” Automatica, vol. 41, no. 7, pp. 1191--2000, 2005.
    3. E. Bullinger and F. Allgöwer, “Adaptive $łambda$-tracking for nonlinear higher relative degree systems,” Automatica, vol. 41, no. 7, pp. 1191--2000, 2005.
    4. M. Diehl, R. Findeisen, H. G. Bock, J. P. Schlöder, and F. Allgöwer, “Nominal stability of the real-time iteration scheme for nonlinear  model predictive control,” IEE Control Theory Appl., vol. 152, no. 3, pp. 296–308, 2005.
    5. C. Ebenbauer, T. Raff, and F. Allgöwer, “Passivity-based Feedback Design for Polynomial Control Systems,” at-Automatisierungstechnik, vol. 8, pp. 356–366, 2005.
    6. T. Eißing, F. Allgöwer, and E. Bullinger, “Robustness properties of apoptosis models with respect to parameter  variations and stochastic influences,” IEE Systems Biology, vol. 152, no. 4, pp. 221–228, 2005.
    7. A. Stemmer, G. Schitter, J. M. Rieber, and F. Allgöwer, “Control strategies towards faster quantitative imaging in atomic  force microscopy,” European J. Control, vol. 11, no. 4–5, pp. 384--395, 2005.
    8. A. Stemmer, G. Schitter, J. M. Rieber, and F. Allgöwer, “Control strategies towards faster quantitative imaging in atomic force microscopy,” European J.\ Control, vol. 11, no. 4–5, pp. 384--395, 2005.
    9. G. L. Wang, M. Zeitz, and F. Allgöwer, “Flatness-based optimal noncausal output transitions for constrained  nonlinear systems: Case study on an isothermal continuously stirred  tank reactor,” IEE Control Theory Appl., vol. 152, no. 1, pp. 105–112, 2005.
    10. G. Weidl, A. L. Madsen, and S. Israelsson, “Applications of Object-Oriented Bayesian Networks for Condition Monitoring,  Root Cause Analysis and Decision Support on Operation of Complex  Continuous Processes,” Comp. & Chem. Eng., vol. 29, pp. 1996–2009, 2005.
    11. T. Raff, C. Ebenbauer, R. Findeisen, and F. Allgöwer, “Remarks on Moving Horizon State Estimation with Guaranteed Convergence,” in Control and Observer Design for Nonlinear Finite and Infinite Dimensional  Systems, no. 322, T. Meurer, K. Graichen, and E. D. Gilles, Eds. Springer Berlin / Heidelberg, 2005, pp. 67–80.
    12. T. Raff, R. Findeisen, C. Ebenbauer, and F. Allgöwer, “Nonlinear Model Predictive Control and Sum of Squares Techniques,” in Fast Motions in Biomechanics and Robotics - Optimization and Feedback  Control, vol. 340, M. Diehl and K. Mombaur, Eds. Springer Berlin / Heidelberg, 2005, pp. 325–344.
    13. I. Alvarado, R. Findeisen, P. Kühl, D. Limón, and F. Allgöwer, “State Estimation for Repetitive Processes Using Iteratively Improving  Moving Horizon Observers,” in Proc. 44th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Seville, Spain, 2005, pp. 7756–7761.
    14. I. Alvarado, R. Findeisen, P. Kühl, D. Limón, and F. Allgöwer, “State Estimation for Repetitive Processes Using Iteratively Improving Moving Horizon Observers,” in Proc.\ 44th IEEE Conf.\ Decision and Control (CDC), European Control Conf.\ (ECC), Seville, Spain, 2005, pp. 7756–7761.
    15. R. Bars et al., “Theory, algorithms and technology in the design of control systems,” in Proc. 16th IFAC World Congress, Prague, Czech Republic, 2005, pp. 122–131.
    16. E. Bullinger, “System Analysis of a Programmed Cell Death Model,” in Proc. 44th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), 2005, pp. 7994–7999.
    17. C. Cimatoribus, T. Eißing, N. Elvassore, F. Allgöwer, and E. Bullinger, “Model discrimination tools in apoptosis,” in Proc. 3rd Foundations of Systems Biology in Engineering (FOSBE), Santa Barbara, CA, USA, 2005, pp. 197–200.
    18. C. Ebenbauer, T. Raff, and F. Allgöwer, “A duality-based LPV Approach to Polynomial State Feedback Design,” in Proc. American Control Conf. (ACC), Portland, OR, USA, 2005, pp. 703–708.
    19. C. Ebenbauer, J. Renz, and F. Allgöwer, “Polynomial Feedback and Observer Design using Nonquadratic Lyapunov  Functions,” in Proc. 44th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Seville, Spain, 2005, pp. 7587–7592.
    20. C. Ebenbauer, T. Raff, and F. Allgöwer, “A Simple Separation Result for Control Affine Systems,” in Proc. 16th IFAC World Congres, 2005.
    21. T. Eißing, C. Cimatoribus, F. Allgöwer, P. Scheurich, and E. Bullinger, “System Properties of the Core Reactions of Apoptosis,” in 1st FEBS Advanced Lecture Course Systems Biology, Gosau, Austria, 2005, p. 164.
    22. R. Findeisen and F. Allgöwer, “Robustness Properties and Output Feedback of Optimization Based Sampled-data  Open-loop feedback,” in Proc. 44th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Seville, Spain, 2005, pp. 54–59.
    23. C. Hüttner, J. M. Rieber, F. Allgöwer, and J. Hugel, “Compensation of time-varying harmonic disturbances on nonlinear  bearingless slice motors,” in Proc. 16th IFAC World Congress, Prague, Czech Republic, 2005.
    24. U. Münz and P. J. Zufiria, “Parametric Fault Diagnosis in Stochastic Dynamical Systems,” in Proceedings of the 19th CEDYA 2005, Madrid, Spain, 2005.
    25. Z. K. Nagy, B. Mahn, F. Ruediger, and F. Allgöwer, “Nonlinear model predictive control of batch processes: an industrial  case study,” in Proc. 16th IFAC World Congress, Prague, Czech Republic, 2005.
    26. Z. K. Nagy, R. Roman, S. P. Agachi, and F. Allgöwer, “A real-time approach for moving horizon estimation based nonlinear  model predictive control of a fluid catalytic cracking unit,” in Proc. 7th World Congress of Chemical Engineering, Glasgow, Scotland, 2005, pp. 504–510.
    27. I. R. Ofiteru, V. Lavric, F. Allgöwer, and E. Bullinger, “Sensitivity Analysis of Escherichia coli’s Tricarboxilic  Acid Cycle under Anaerobic Conditions,” in Proc. 3rd Foundations of Systems Biology in Engineering (FOSBE), Santa Barbara, CA, USA, 2005, pp. 337--340.
    28. T. Raff, P. H. Menold, C. Ebenbauer, and F. Allgöwer, “A Finite Time Functional Observer for Linear Systems,” in Proc. 44th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Seville, Spain, 2005, pp. 7198–7203.
    29. T. Raff, C. Ebenbauer, and F. Allgöwer, “Nonlinear Model Predictive Control: A Passivity-based Approach,” in International Workshop on Assessment and Future Directions of Nonlinear  Model Predictive Control, 2005.
    30. A. Rehm and F. Allgöwer, “$H_ınfty$ Control of Descriptor Systems in a Differential Inclusion  Setting,” in Proc. American Control Conf. (ACC), Portland, OR, USA, 2005, pp. 4303–4308.
    31. J. M. Rieber, A. Fritsch, and F. Allgöwer, “State-space formulas for gain-scheduled $\ell_1$-optimal controllers,” in Proc. American Control Conf. (ACC), Portland, OR, USA, 2005, pp. 609--614.
    32. J. M. Rieber, G. Schitter, A. Stemmer, and F. Allgöwer, “Experimental application of $\ell_1$-optimal control in atomic  force microscopy,” in Proc. 16th IFAC World Congress, Prague, Czech Republic, 2005.
    33. R. Roman, Z. K. Nagy, F. Allgöwer, and S. P. Agachi, “Dynamic Modeling and Nonlinear Model Predictive Control of a Fluid  Catalytic Cracking Unit,” in Proc. 15th European Symp. Computer Aided Process Engineering (ESCAPE), Barcelona, Spain, 2005, pp. 1363–1368.
    34. R. Roman, Z. K. Nagy, F. Allgöwer, S. P. Agachi, and M. Cristea, “Complex dynamic modeling and linear model predictive control of a  fluid catalytic cracking process,” in Proc. 14th Romanian Int. Conf. Chemistry and Chemical Engineering  (RICCE), Bucharest, Romania, 2005, pp. 116–123.
    35. T. Sauter et al., “Mathematical modeling of TNF induced apoptotic and anti-apoptotic  crosstalk in mammalian cells,” in 6th International Conference on Systems Biology, Boston, MA, 2005.
    36. T. Sauter, J. Saez-Rodriguez, H. Conzelmann, T. Eißing, E. D. Gilles, and P. Scheurich, “Cellect, project C4: Mathematical modeling of cellular regulation  networks of apoptosis and cell proliferation.,” in 2nd BMBF Colloquium “Proteomics,” Potsdam, Germany, 2005.
    37. T. Sauter, J. Saez-Rodriguez, H. Conzelmann, T. Eißing, E. D. Gilles, and P. Scheurich, “Cellect, project C4: Mathematical modeling of cellular regulation networks of apoptosis and cell proliferation.,” in 2nd BMBF Colloquium “Proteomics,” Potsdam, Germany, 2005.
    38. T. Schweickhardt and F. Allgöwer, “Linear modeling error and steady-state behaviour of nonlinear dynamical  systems,” in Proc. 44th IEEE Conf. Decision and Control (CDC), European Control  Conf. (ECC), Seville, Spain, 2005, pp. 8150–8155.
    39. P. Wolfrum, A. Vargas, M. Gallivan, and F. Allgöwer, “Complexity reduction of a thin film deposition model using a trajectory  based nonlinear model reduction technique,” in Proc. American Control Conf. (ACC), Portland, OR, USA, 2005, pp. 2566–2571.
    40. T. Eißing, P. Scheurich, and F. Allgöwer, “To Be or Not to Be - Mathematical Systems Theory to  Analyze Biological Signal Processing.” 2005.
    41. R. Findeisen and F. Allgöwer, “Modeling and Analysis of Bone Growth and Remodeling.” 2005.
    42. R. Findeisen and F. Allgöwer, “Output-Feedback Nonlinear Model Predictive Control for Chemical Processes  without the Need of Fast Observers.” 2005.
    43. R. Roman, Z. K. Nagy, S. P. Agachi, and F. Allgöwer, “First principles modeling and nonlinear optimization based estimation  and control of a fluid catalytic cracking unit.” 2005.
    44. C. Ebenbauer, “Polynomial Control Systems: Analysis and Design via Dissipation Inequalities  and Sum of Square,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2005.
    45. J. M. Rieber and F. Allgöwer, “Mixed $\ell_1/H_ınfty$ control of MIMO systems:  a linear matrix inequality approach,” Institute for Systems Theory in Engineering, University of Stuttgart, Stuttgart, Germany, Technical report, 2005.
    46. G. Weidl, A. L. Madsen, and S. Israelsson, “Object-Oriented Bayesian Networks for Condition Monitoring, Root  Cause Analysis and Decision Support on Operation of Complex Continuous  Processes: Methodology & Applications,” Institute for Systems Theory in Engineering, IST Preprint Series 2005-1, 2005.
    47. T. Eißing, “Mathematical Modeling and System Analysis of Tumor Necrosis  Factor Signaling Pathways,” 2005.
  16. 2004

    1. F. Allgöwer, R. Findeisen, and Z. Nagy, “Nonlinear Model Predictive Control: From Theory to Application,” J. Chin. Inst. Chem. Eng., vol. 35, no. 3, pp. 299–315, 2004.
    2. H. Conzelmann, J. Saez-Rodriguez, T. Sauter, E. Bullinger, F. Allgöwer, and E. D. Gilles, “Reduction of mathematical models of signal transduction networks:  Simulation-based approach applied to EGF receptor signaling,” IEE Systems Biology, vol. 1, no. 1, pp. 159--169, 2004.
    3. T. Eißing, H. Conzelmann, E. D. Gilles, F. Allgöwer, E. Bullinger, and P. Scheurich, “Bistability analyses of a caspase activation model for receptor induced  apoptosis.,” J. Biol. Chem., vol. 279, no. 35, pp. 36892–36897, 2004.
    4. A. Kremling et al., “A benchmark for methods in reverse engineering and model discrimination:  problem formulation and solutions,” Genome Research, vol. 14, no. 9, pp. 1773--1785, 2004.
    5. A. Rehm and F. Allgöwer, “$H_ınfty$ Regelung von zeitdiskreten Deskriptorsystemen,” at-Automatisierungstechnik, vol. 52, no. 9, pp. 440–445, 2004.
    6. A. Rehm and F. Allgöwer, “$H_ınfty$ Regelung von zeitdiskreten Deskriptorsystemen,” at-Automatisierungstechnik, vol. 52, no. 9, pp. 440–445, 2004.
    7. J. M. Rieber and D. G. Taylor, “Integrated control system and mechanical design of a compliant  two-axes mechanism,” Mechatronics, vol. 14, no. 9, pp. 1069--1087, 2004.
    8. J. M. Rieber, H. Wehlan, and F. Allgöwer, “The ROBORACE contest,” IEEE Control Systems Magazine, vol. 24, no. 5, pp. 57--60, 2004.
    9. T. Sauter and E. Bullinger, “Detailed mathematical modeling of metabolic and regulatory networks,” BIOforum Europe, vol. 2004, no. 2, pp. 62–64, 2004.
    10. G. Schitter, A. Stemmer, and F. Allgöwer, “Robust two-degree-of-freedom control of an atomic force microscope,” Asian J. Control, vol. 6, no. 2, pp. 156–163, 2004.
    11. G. Schitter, F. Allgöwer, and A. Stemmer, “A new control strategy for high-speed atomic force microscopy,” Nanotechnology, vol. 15, pp. 108–114, 2004.
    12. P. Schumm, T. Schweickhardt, E. Bullinger, and F. Allgöwer, “Integration und Interaktion: Möglichkeiten des Einsatzes  von Notebook und Internet in der regelungstechnischen Ausbildung,” at-Automatisierungstechnik, vol. 2, no. 2, pp. 81–89, 2004.
    13. F. Allgöwer and M. Zeitz, Eds., Nonlinear Control Systems 2004. Oxford, UK: Elsevier, 2004.
    14. F. Allgöwer and F. Gao, Eds., Advanced Control of Chemical Processes. Oxford, UK: Elsevier, 2004.
    15. A. Rehm, Control of Linear Descriptor Systems: A Matrix Inequality Approach. Düsseldorf: Fortschr.-Ber. VDI Reihe 8 Nr. 1019, VDI Verlag, 2004.
    16. T. Schweickhardt and F. Allgöwer, “Quantitative nonlinearity assessment -- An introduction to nonlinearity  measures,” in The Integration of Design and Control, M. Georgiadis and P. Seferlis, Eds. Elsevier Science, 2004, pp. 76–95.
    17. C. Ebenbauer and F. Allgöwer, “Computer-aided stability analysis of differential-algebraic equations,” in Proc. 6th IFAC Symp. Nonlinear Control Systems (NOLCOS), Stuttgart, Germany, 2004, pp. 1025--1029.
    18. C. Ebenbauer, R. Findeisen, and F. Allgöwer, “Nonlinear High-Gain Observer Design via Semidefinite Programming,” in Proc. 2nd IFAC Symp. Systems, Structure, and Control (SSSC), Oaxaca, Mexico, 2004, pp. 751–756.
    19. C. Ebenbauer and F. Allgöwer, “Minimum-Phase Property of Nonlinear Systems in Terms of a Dissipation  Inequality,” in Proc. American Control Conf. (ACC), Boston, MA, USA, 2004, pp. 1737--1742.
    20. T. Eißing, H. Conzelmann, E. D. Gilles, F. Allgöwer, E. Bullinger, and P. Scheurich, “Mathematical modeling applied to caspase activation reveals a requirement  for additional control,” in 5th International Conference on Systems Biology, Heidelberg, Germany, 2004, p. 207.
    21. T. Eißing, H. Conzelmann, E. D. Gilles, F. Allgöwer, E. Bullinger, and P. Scheurich, “Mathematical modeling and system analysis of caspase activation,” in International Workshop on Theoretical Biophysics, Hiddensee Island, Germany, 2004, p. 11.
    22. T. Eißing et al., “Mathematical modeling applied to caspase activation downstream of  death receptors: A missing guardian for caspase 8,” in 2nd International Symposium of the SFB 495, Hohenheim, Germany, 2004.
    23. R. Findeisen and F. Allgöwer, “Stabilization Using Sampled-data Open-Loop Feedback -- a Nonlinear  Model Predictive Control Perspective,” in Proc. 6th IFAC Symp. Nonlinear Control Systems (NOLCOS), Stuttgart, Germany, 2004, pp. 735–740.
    24. R. Findeisen and F. Allgöwer, “Min-max output feedback predictive control with guaranteed stability,” in Proc. Int. Symp. Mathematical Theory of Networks and Systems (MTNS), Katholieke Universiteit Leuven, Belgium, 2004.
    25. R. Findeisen and F. Allgöwer, “Computational Delay in Nonlinear Model Predictive Control,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), Hong Kong, China, 2004, pp. 427–432.
    26. R. Lepore, R. Findeisen, A. Vande Wouwer, F. Allgöwer, and M. Remy, “On open- and closed-loop control of an MMA polymerization reactor,” in Proc. 23rd Benelux Meeting on Systems and Control, Helvoirt, The Netherlands, 2004.
    27. R. Lepore, R. Findeisen, Z. K. Nagy, F. Allgöwer, and A. Vande Wouwer, “Optimal Open- and Closed-Loop Control for Disturbance Rejection in  Batch Process Control: a MMA Polymerization Example,” in Proc. Symp. Knowledge Driven Batch Processes (BatchPro), Poros, Greece, 2004, pp. 235–241.
    28. Z. Nagy, R. Findeisen, and F. Allgöwer, “Hierarchical nonlinear model predictive control of an industrial  batch reactor,” in Proc. Symp. Knowledge Driven Batch Processes (BatchPro), Poros, Greece, 2004, pp. 203–210.
    29. Z. K. Nagy, F. Allgöwer, F. Ruediger, and B. Mahn, “Efficient tool for nonlinear model predictive control of batch processes,” in Proc. 12th Mediterranean Conf. Control and Automation (MED), Kusadasi, Turkey, 2004, pp. 1128–1134.
    30. Z. K. Nagy and S. P. Agachi, “Internet-based interactive remote laboratory for educational experiments,” in Proc. of the AIChE Annual Meeting, 2004.
    31. Z. K. Nagy and F. Allgöwer, “Nonlinear model predictive control: from chemical industries to microelectronics,” in Proc. 43rd IEEE Conf. Decision and Control (CDC), Atlantis, Paradise Island, Bahamas, 2004, pp. 4249–4254.
    32. T. Raff, C. Ebenbauer, and F. Allgöwer, “Feedback Passivation of an Electrostatic Microactuator: A Semidefinite  Programming Approach,” in Proc. 6th IFAC Symp. Nonlinear Control Systems (NOLCOS), Stuttgart, Germany, 2004, pp. 1181–1186.
    33. T. Raff, C. Ebenbauer, and F. Allgöwer, “Passivity-based Nonlinear Dynamic Output Feedback Design: A Semidefinite  Programming Approach,” in Proc. 43rd IEEE Conf. Decision and Control (CDC), Atlantis, Paradise Island, Bahamas, 2004, pp. 5409–5414.
    34. T. Raff, R. Findeisen, C. Ebenbauer, and F. Allgöwer, “Model Predictive Control of Discrete Time Polynomial Control Systems:  A Convex Approach,” in Proc. 2nd IFAC Symp. Systems, Structure, and Control (SSSC), Oaxaca, Mexico, 2004, pp. 158–163.
    35. T. Raff, C. Ebenbauer, and F. Allgöwer, “Passivity-based Nonlinear Dynamic Output Feedback Design: A Semidefinite Programming Approach,” in Proc.\ 43rd IEEE Conf.\ Decision and Control (CDC), Atlantis, Paradise Island, Bahamas, 2004, pp. 5409–5414.
    36. A. Rehm and F. Allgöwer, “$H_ınfty$ control of descriptor systems: An application from  binary distillation control,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), Hong Kong, China, 2004, pp. 351–356.
    37. A. Rehm and F. Allgöwer, “Causal  $ H_ınfty$ Control of Discrete-time Descriptor Systems:  An LMI Approach in two Steps,” in Proc. 16th Int. Symp. Mathematical Theory of Networks and Systems  (MTNS), Leuven, Belgium, 2004.
    38. Y. Shastri, T. Schweickhardt, and F. Allgöwer, “Plant and Control-relevant Nonlinearity Analysis of a CSTR: a Case  Study,” in Proc. 7th IFAC Symp. Dynamics and Control of Process Systems (DYCOPS), Cambridge, MA, USA, 2004, pp. 89–94.
    39. A. Vargas and F. Allgöwer, “Model reduction for process control using iterative nonlinear identification,” in Proc. American Control Conf. (ACC), Boston, MA, USA, 2004, pp. 2915–2920.
    40. G. Weidl, M. Rode, A. Horch, C. Shaw, and A. Vollmer, “Automatische Ursachen Analyse von Fehlern und Störungen in Walzwerken  - eine Übersicht und praktische Beispiele,” in Tagungsband 5.AKIDA, Aachener Kolloquium f�r Instandhaltung, Diagnose  und Anlagen�berwachung, Aachen,Germany, 2004, pp. 399–410.
    41. G. Weidl, “Adaptive Risk Assessment in Complex Large Scale Processes with Reduced  Computational Complexity,” in 9th International Conference on Industrial Engineering Theory, Applications  and Practice, Auckland, New Zealand, 2004, pp. 72–78.
    42. A. Yonchev, R. Findeisen, C. Ebenbauer, and F. Allgöwer, “Model Predictive Control of Linear Continuous Time Singular Systems  Subject to Input Constraints,” in Proc. 43rd IEEE Conf. Decision and Control (CDC), Atlantis, Paradise Island, Bahamas, 2004, pp. 2047–2052.
    43. R. Findeisen and F. Allgöwer, “Prädiktive Ausgangsregelung mit Stabilitätsgarantie.” 2004.
    44. R. Findeisen, T. Raff, and F. Allgöwer, “Prädiktive Ausgangsregelung nichtlinearer verfahrenstechnischer  Prozesse: Praktische und theoretische Aspekte.” 2004.
    45. Z. Nagy, R. Findeisen, and F. Allgöwer, “Nonlinear model predictive control approach for robust run-to-run  control of batch processes.” 2004.
    46. Z. K. Nagy, R. Findeisen, and F. Allgöwer, “Nonlinear Model Predictive Control of Batch Processes.” 2004.
    47. Z. K. Nagy, R. Findeisen, and F. Allgöwer, “Nonlinear Model Predictive Control of Batch Processes.” 2004.
    48. J. M. Rieber, F. Allgöwer, and A. Stemmer, “Schneller sehen durch Regelungstechnik -- Moderne Bildgebung  in der Nanotechnologie.” 2004.
    49. R. Findeisen, “Nonlinear model predictive control : a sampled data feedback perspective,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2004.
    50. P. H. Menold, “Finite and Asymptotic Time State Estimation for Linear and Nonlinear  Systems,” Institute for Systems Theory and Automatic Control, University of  Stuttgart, Stuttgart, Germany, 2004.
    51. T. Schweickhardt and F. Allgöwer, “Trajectory-based approximate optimal controller synthesis,” Institute for Systems Theory in Engineering, University of Stuttgart,  Germany, 2004.
  17. 2003

    1. M. Diehl et al., “An Efficient Approach for Nonlinear Model Predictive Control of Large-Scale  Systems. Part II: Experimental Evaluation Considering the Control  of a Distillation Column,” at-Automatisierungstechnik, vol. 51, no. 1, pp. 22–29, 2003.
    2. M. Ederer, T. Sauter, E. Bullinger, E. D. Gilles, and F. Allgöwer, “An Approach for Dividing Models of Biological Reaction Networks into  Functional Units,” Simulation: Trans. Society for Modeling and Simulation International, vol. 79, no. 12, pp. 703--716, 2003.
    3. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “State and Output Feedback Nonlinear Model Predictive Control: An  Overview,” European J. Control, vol. 9, no. 2–3, pp. 179–195, 2003.
    4. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Output Feedback Stabilization for Constrained Systems with Nonlinear  Model Predictive Control,” Int. J. Robust and Nonlinear Control, vol. 13, no. 3–4, pp. 211–227, 2003.
    5. L. Imsland, R. Findeisen, F. Allgöwer, and B. A. Foss, “Output feedback stabilization with nonlinear predictive control:  Asymptotic properties,” Int. J. Modelling, Identification and Control, vol. 24, no. 3, pp. 169–179, 2003.
    6. L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B. A. Foss, “A note on stability, robustness and performance of output feedback  nonlinear model predictive control.,” J. Proc. Contr., vol. 13, no. 7, pp. 633–644, 2003.
    7. L. Magni, G. de Nicolao, R. Scattolini, and F. Allgöwer, “Robust model predictive control for nonlinear discrete-time systems,” Int. J. Robust and Nonlinear Control, vol. 13, no. 3–4, pp. 229–246, 2003.
    8. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Towards a Sampled-Data Theory for Nonlinear Model Predictive Control,” in New Trends in Nonlinear Dynamics and Control, and their Applications, vol. 295, C. Kang, M. Xiao, and W. Borges, Eds. Springer Berlin / Heidelberg, 2003, pp. 295–311.
    9. R. Findeisen and F. Allgöwer, “The Quasi-Infinite Horizon Approach to Nonlinear Model Predictive  Control,” in Nonlinear and Adaptive Control, vol. 281, A. Zinober and D. Owens, Eds. Springer Berlin / Heidelberg, 2003, pp. 89–108.
    10. M. Diehl, R. Findeisen, F. Allgöwer, J. P. Schlöder, and H. G. Bock, “Stability of Nonlinear Model Predictive Control in the Presence of  Errors due to Numerical Online Optimization,” in Proc. 42nd IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2003, pp. 1419–1424.
    11. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Stability Conditions for Observer Based Output Feedback Stabilization  with Nonlinear Model Predictive Control,” in Proc. 42nd IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2003, pp. 1425--1430.
    12. R. Findeisen and F. Allgöwer, “Theorie und Anwendung der nichtlinearen prädiktiven Regelung,” in Proc. of GMA-Gesellschaft für Meß- und Automatisierungstechnik  annual meeting, Baden-Baden, Germany, 2003.
    13. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Output-feedback Nonlinear Model Predictive Control using High-Gain  Observers in Original Coordinates,” in Proc. European Control Conf. (ECC), Cambridge, UK, 2003, pp. 2061–2066.
    14. R. Findeisen and F. Allgöwer, “Theorie und Anwendung der nichtlinearen prädiktiven Regelung,” in Proc. of GMA-Gesellschaft für Meß- und Automatisierungstechnik annual meeting, Baden-Baden, Germany, 2003.
    15. N. Hernjak, F. J. Doyle III, F. Allgöwer, and T. Schweickhardt, “Relationship between control-relevant nonlinearity and performance  objective,” in IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Hong Kong, China, 2003, pp. 543–548.
    16. P. H. Menold, R. Findeisen, and F. Allgöwer, “Finite time convergent observers for linear time-varying systems,” in Proc. 11th Mediterranean Conf. Control and Automation (MED), Rhodes, Greece, 2003.
    17. P. H. Menold, R. Findeisen, and F. Allgöwer, “Finite time convergent observers for nonlinear systems,” in Proc. 42nd IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2003, pp. 5673--5678.
    18. P. H. Menold and F. Allgöwer, “Finite time convergent observer,” in AIChE Annual Meeting, San Francisco, CA, USA, 2003.
    19. A. Rehm and F. Allgöwer, “$H_ınfty$ control of descriptor systems: An application from  binary distillation control,” in Proc. European Control Conf. (ECC), Cambridge, UK, 2003.
    20. J. M. Rieber and F. Allgöwer, “An approach to gain-scheduled $\ell_1$-optimal control of linear  parameter-varying systems,” in Proc. 42nd IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2003, pp. 6109--6114.
    21. G. Schitter, A. Stemmer, and F. Allgöwer, “Robust 2DOF-control of a piezoelectric tube scanner for high speed  atomic force microscopy,” in Proc. American Control Conf. (ACC), Denver, CO, USA, 2003, pp. 3720–3725.
    22. P. Schumm, T. Schweickhardt, E. Bullinger, and F. Allgöwer, “Der Einsatz neuer Medien in der regelungstechnischen Ausbildung,” in Proc. GMA-Kongress, Baden-Baden, Germany, 2003, pp. 1061–1068.
    23. T. Schweickhardt and F. Allgöwer, “How Nonlinear is Nonlinear? An Approach to Nonlinearity Quantification,” in Proceedings of the 7th Philips Conference on Applications of Control  Technology (PACT’03), 2003, pp. 1–14.
    24. T. Schweickhardt, F. Allgöwer, and F. J. Doyle III, “Nonlinearity quantification for the optimal state feedback controller,” in Proc. European Control Conf. (ECC), Cambridge, U.K., 2003, pp. 4611–4617.
    25. T. Schweickhardt, F. Allgöwer, and F. J. Doyle III, “The optimal control law nonlinearity measure: Improving control-relevant  nonlinearity assessment,” in AIChE Annual Meeting, San Francisco, CA, USA, 2003.
    26. F. Allgöwer, R. Findeisen, and C. Ebenbauer, “Nonlinear Model Predictive Control.” 2003.
    27. T. Eißing, “Modeling Signal Transduction Pathways at the Example of Tumor Necrosis  Factor signaling,” 2003.
    28. R. Findeisen and F. Allgöwer, “Nonlinear Model Predictive Control,” 2003.
  18. 2002

    1. M. Diehl et al., “An Efficient Approach for Nonlinear Model Predictive Control of Large-Scale  Systems Part I: Description of the Methodology,” at-Automatisierungstechnik, vol. 50, no. 12, pp. 557–567, 2002.
    2. M. Diehl, R. Findeisen, Z. Nagy, H. G. Bock, J. P. Schlöder, and F. Allgöwer, “Real-time optimization and Nonlinear Model Predictive Control of  Processes governed by Differential-Algebraic Equations,” J. Proc. Contr., vol. 4, no. 12, pp. 577–585, 2002.
    3. A. Rehm and F. Allgöwer, “General quadratic performance analysis and synthesis of differential  algebraic equation (DAE) systems,” J. Proc. Contr., vol. 12, no. 4, pp. 467–474, 2002.
    4. F. Allgöwer, Z. Nagy, and R. Findeisen, “Nonlinear Model Predictive Control: From Theory to Application,” in Proc. Int. Symp. Design, Operation and Control of Chemical Plants  (PSE), Taipei, Taiwan, 2002, pp. 639–650.
    5. E. Bullinger, T. Sauter, F. Allgöwer, and E. D. Gilles, “On deriving a hybrid model for Carbohydrate Uptake in Escherichia  col,” in Proc. 15th IFAC World Congress, Barcelona, Spain, 2002.
    6. E. Bullinger, T. Sauter, F. Allgöwer, and E. D. Gilles, “On deriving a hybrid model for Carbohydrate Uptake in Escherichia col,” in Proc.\ 15th IFAC World Congress, Barcelona, Spain, 2002.
    7. C. Burger, E. Bullinger, S. Papakosta, and T. Wagner, “Context awareness for application sharing in teaching environment,” in Proc. Int. Conf. Advances in Infrastructure for e-Business, e-Education,  e-Science, and e-Medicine on the Internet (SSGRR), 2002.
    8. C. Burger, S. Papakosta, E. Bullinger, and T. Wagner, “Investigation of application sharing systems for teaching purposes  in engineering disciplines,” in Proc. 4th Int. Conf. New Educational Environment, 2002.
    9. R. Findeisen et al., “Computation and Performance Assesment of Nonlinear Model Predictive  Control,” in Proc. 41st IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2002, pp. 4613–4618.
    10. R. Findeisen, M. Diehl, T. Bürner, F. Allgöwer, H. G. Bock, and J. P. Schlöder, “Efficient Output Feedback Nonlinear Model Predictive Control,” in Proc. American Control Conf. (ACC), Anchorage, AK, USA, 2002, pp. 4752–4757.
    11. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Output feedback nonlinear predictive control - A separation principle  approach.,” in Proc. 15th IFAC World Congress, Barcelona, Spain, 2002.
    12. H. W. Knobloch, C. Ebenbauer, and F. Allgöwer, “A framework for disturbance attenuation with discontinuous control,” in Proc. 15th IFAC World Congres, Barcelona, Spain, 2002.
    13. L. Magni, G. de Nicolao, R. Scattolini, and F. Allgöwer, “Robust receding horizon control for nonlinear discrete-time systems,” in Proc. 15th IFAC World Congress, Barcelona, Spain, 2002.
    14. Z. Nagy et al., “The tradeoff between modelling complexity and real-time feasibility  in nonlinear model predictive control.,” in Proc. 6th World Multiconference on Systemics, Cybernetics and Informatics  (SCI), Orlando, FL, USA, 2002, pp. 329–334.
    15. A. Rehm and F. Allgöwer, “An LMI Approach towards $H_ınfty$ Control of Discrete-time  Descriptor Systems,” in Proc. American Control Conf. (ACC), Minneapolis, MN, USA, 2002, pp. 614–619.
    16. A. Rehm and F. Allgöwer, “An LMI Approach towards Stabilization of Discrete-time Descriptor  Systems,” in Proc. 15th IFAC World Congress, Barcelona, Spain, 2002.
    17. C. W. Scherer, H. Chen, and F. Allgöwer, “Disturbance Attenuation with Actuator Constraints by Hybrid State  Feedback Control,” in Proc. 41st IEEE Conf. Decision and Control (CDC), Las Vegas, NV, USA, 2002, pp. 4134–4139.
    18. B. Schoeberl, T. Eißing, M. Fotin, E. D. Gilles, and P. Scheurich, “A mathematical model of TNF receptor interaction,” in 1st International Symposium of the SFB 495, Stuttgart, Germany, 2002.
    19. F. Allgöwer et al., “Tutorial workshop on computational efficiency in linear and non-linear  predictive control.” 2002.
    20. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Output Feedback Nonlinear Predictive Control.” 2002.
    21. L. Imsland, R. Findeisen, F. Allgöwer, and B. A. Foss, “Output Feedback Nonlinear Model Predictive Control.” 2002.
    22. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Semi-regional stability results for output-feedback nonlinear predictive  control,” 2002.
  19. 2001

    1. G. Schitter, P. H. Menold, H. F. Knapp, F. Allgöwer, and A. Stemmer, “High performance feedback for fast scanning atomic force microscopes,” Review of Scientific Instruments, vol. 72, no. 8, pp. 3320–3327, 2001.
    2. M. Diehl et al., “Real-Time Optimization of Large Scale Process Models: Nonlinear Model  Predictive Control of a High Purity Distillation Column,” in Online Optimization of Large Scale Systems: State of the  Art, M. Grötschel, S. O. Krumke, and J. Rambau, Eds. Springer Berlin / Heidelberg, 2001, pp. 363–384.
    3. E. Bullinger, R. Findeisen, and F. Allgöwer, “Adaptive $łambda$-Tracking of Nonlinear Systems with Higher Relative  Degree Using Reduced-Order High Gain Control,” in Proc. 5th IFAC Symp. Nonlinear Control Systems (NOLCOS), St. Petersburg, Russia, 2001, pp. 92–97.
    4. H. Chen and F. Allgöwer, “Nonlinear model predictive control of a class of mechatronic systems,” in Proc. 4th China-Korea Joint Workshop on Process Systems Engineering, Guangzhou, China, 2001, pp. 65–72.
    5. R. Findeisen, Z. Nagy, M. Diehl, F. Allgöwer, H. G. Bock, and J. P. Schlöder, “Computational feasibility and performance of nonlinear model predicitve  control.,” in Proc. European Control Conf. (ECC), Porto, Portugal, 2001, pp. 957--961.
    6. L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B. A. Foss, “On Output feedback Nonlinear Model Predictive Control using high  gain observers for a class of systems,” in Proc. 6th IFAC Symp. Dynamics and Control of Process Systems (DYCOPS), Jejudo, Korea, 2001, pp. 91–96.
    7. A. Kremling, T. Sauter, E. Bullinger, M. Ederer, F. Allgöwer, and E. D. Gilles, “Biosystems Engineering: Applying methods from systems theory to biological  systems,” in Proc. 2nd Int. Conf. Systems Biology, Pasadena, CA, USA, 2001, pp. 282--290.
    8. Z. Nagy et al., “Using Genetic Algorithm in Robust Nonlinear Model Predictive Control,” in Proc. 11th European Symp. Computer Aided Process Engineering (ESCAPE), Kolding, Denmark, 2001, pp. 711–716.
    9. Z. Nagy, S. P. Agachi, F. Allgöwer, and R. Findeisen, “Nonlinear model predictive control of a high purity distillation  column,” in 14-th International Congress of Chemical and Process Engineering  CHISA 2000, Prague, Czech Republic, 2001.
    10. M. Niethammer, P. H. Menold, and F. Allgöwer, “Parameter and Derivative Estimation for Nonlinear Continuous-Time  System Identification,” in Proc. 5th IFAC Symp. Nonlinear Control Systems (NOLCOS), St. Petersburg, Russia, 2001, pp. 691–696.
    11. R. K. Pearson, P. H. Menold, and F. Allgöwer, “Structured Outliers and Data Cleaning Filters,” in Proceedings of the IEEE-EURASIP Nonlinear Signal and Image Processing  workshop, NSIP-01, Baltimore, MD, USA, 2001.
    12. F. Allgöwer and R. Findeisen, “Nonlinear Model Predictive Control of Chemical Processes.” 2001.
    13. F. Allgöwer and R. Findeisen, “Nonlinear Model Predictive Control of Chemical Processes.” 2001.
    14. R. Findeisen and F. Allgöwer, “An introduction to nonlinear model predictive control.” 2001.
  20. 2000

    1. R. Findeisen and F. Allgöwer, “A Nonlinear Model Predictive Control Scheme for the Stabilization  of Setpoint Families,” Journal A, Benelux Quarterly Journal on Automatic Control, vol. 41, no. 1, pp. 37--45, 2000.
    2. R. Findeisen and F. Allgöwer, “A Nonlinear Model Predictive Control Scheme for the Stabilization of Setpoint Families,” Journal A, Benelux Quarterly Journal on Automatic Control, vol. 41, no. 1, pp. 37--45, 2000.
    3. C. W. Frei, E. Bullinger, A. Gentilini, A. H. Glattfelder, T. Sieber, and A. M. Zbinden, “Artifact-tolerant controllers for automatic drug delivery in anesthesia,” Crit. Rev. Biomed. Eng., vol. 28, no. 1–2, pp. 187–192, 2000.
    4. A. Rehm and F. Allgöwer, “Self-Scheduled $H_ınfty$ Output Feedback Control of Descriptor  Systems,” Comp. & Chem. Eng., vol. 24, no. 2–7, pp. 279–284, 2000.
    5. R. Findeisen and F. Allgöwer, “Nonlinear model predictive control for index--one DAE systems,” in Nonlinear Model Predictive Control, vol. 26, F. Allgöwer and A. Zheng, Eds. Basel: Birkhäuser, 2000, pp. 145--162.
    6. F. Allgöwer, R. Findeisen, Z. Nagy, M. Diehl, H. G. Bock, and J. P. Schlöder, “Efficient Nonlinear Model Predictive Control for Large Scale Constrained  Processes,” in Proc. 6th Int. Conf. Methods and Models in Automation and Robotics, 2000, pp. 43–54.
    7. E. Bullinger, R. Findeisen, F. J. Kraus, and F. Allgöwer, “Some further Results on Adaptive $łambda$-tracking for Linear Systems  with High Relative Degree,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2000, pp. 3655–3659.
    8. E. Bullinger and F. Allgöwer, “Adaptive $łambda$-tracking for Nonlinear Systems with Higher Relative  Degree,” in Proc. 39th IEEE Conf. Decision and Control (CDC), Sydney, Australia, 2000, pp. 4771–4776.
    9. E. Bullinger, C. W. Frei, T. J. Sieber, A. H. Glattfelder, F. Allgöwer, and A. M. Zbinden, “Adaptive $łambda$-tracking in Anesthesia,” in Proc. 4th IFAC Symp. Modelling and Control in Biomedical Systems, Oxford, UK, 2000, pp. 181–186.
    10. R. Findeisen, H. Chen, and F. Allgöwer, “Nonlinear Predictive Control for Setpoint Families,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2000, pp. 260–264.
    11. R. Findeisen, F. Allgöwer, M. Diehl, H. G. Bock, J. P. Schlöder, and Z. Nagy, “Efficient Nonlinear Model Predictive Control,” in Proc. 6th Int. Conf. Chemical Process Control (CPC), Tuscon, AZ, USA, 2000, pp. 454–460.
    12. Z. Nagy et al., “Real-time Feasibility of Nonlinear Predictive Control for Large Scale  Processes -- a Case Study,” in Proc. American Control Conf. (ACC), Chicago, IL, USA, 2000, pp. 4249--4254.
    13. R. K. Pearson, P. H. Menold, and F. J. Kraus, “Set-theoretic input sequence design for orthonormal model identification,” in Proceedings of the IFAC Symposium on System Identification, SYSID  2000, Santa Barbara, California, USA, 2000.
    14. P. H. Menold, “Set-theoretic input sequence design for orthonormal model identification.” 2000.
    15. P. H. Menold, “Ein Struktureignungsmaß zur Modellstrukturauswahl.” 2000.
    16. G. Schitter, P. H. Menold, H. Knapp, A. Stemmer, and F. Allgöwer, “Simulation and open loop identification of tapping mode atomic force  microscopy.” 2000.
    17. E. Bullinger, “Adaptive $łambda$-tracking for Systems with Higher Relative Degree,” Swiss Federal Institute of Technology (ETH), 2000.
  21. 1999

    1. H. Chen and F. Allgöwer, “A quasi-infinite horizon predictive control scheme for constrained  nonlinear systems,” IEE Control Theory Appl., vol. 16, no. 3, pp. 313–319, 1999.
    2. F. Allgöwer, T. A. Badgwell, J. B. Rawlings, and S. J. Wright, “Nonlinear model predictive control,” in Perspectives in Control. Plenary Lectures and Mini-Courses at the  5th European Control Conference ECC’99, Springer-Verlag, London, 1999, pp. 391–449.
    3. E. Bullinger and F. Allgöwer, “Adaptive $łambda$-tracking for Linear Systems with Higher Relative  Degree --- The Continuous Adaptation Case,” in Proc. European Control Conf. (ECC), Karlsruhe, Germany, 1999.
    4. E. Bullinger, A. Ilchmann, and F. Allgöwer, “Piecewise Constant High-Gain Adaptive $łambda$-tracking for Higher  Relative Degree Linear Systems,” in Proc.\ of the 14th IFAC World Congress, Beijing, China, Beijing, China, 1999, vol. D, pp. 249–254.
    5. C. W. Frei, E. Bullinger, T. Sieber, A. H. Glattfelder, and A. M. Zbinden, “Artefakttolerante Regelungen für die Anästhesie,” in Fortschritts-Berichte VDI, Reihe 17, Nr. 183, AUTOMED '99, Beiträge  zum Workshop Äutomatisierungstechnische Verfahren für die Medizin",  25./26. Februar, Darmstadt, 1999, pp. 37–38.
    6. P. H. Menold, R. K. Pearson, and F. Allgöwer, “Online outlier detection and removal,” in Proc. 7th Mediterranean Conf. Control and Automation (MED), Haifa, Israel, 1999, pp. 1110–1133.
    7. P. H. Menold, “Dealing with Anomalous Data Points.” 1999.
    8. P. H. Menold, “Online outlier detection and removal.” 1999.
    9. G. Schitter, P. H. Menold, H. Knapp, A. Stemmer, and F. Allgöwer, “Intelligent Control for the next generation of fast scanning atomic  force microscopes.” 1999.
    10. G. Schitter, P. H. Menold, H. Knapp, A. Stemmer, and F. Allgöwer, “Intelligent Control for the next generation of fast scanning atomic force microscopes.” 1999.
  22. 1998

    1. H. Chen and F. Allgöwer, “A computationally attractive nonlinear predictive control scheme  with guaranteed stability for stable systems,” J. Proc. Contr., vol. 8, no. 5–6, pp. 475–485, 1998.
    2. H. Chen and F. Allgöwer, “A quasi-infinite horizon nonlinear model predictive control scheme  with guaranteed stability,” Automatica, vol. 34, no. 10, pp. 1205–1217, 1998.
    3. H. Chen and F. Allgöwer, “A computationally attractive nonlinear predictive control scheme with guaranteed stability for stable systems,” J.\ Proc.\ Contr., vol. 8, no. 5–6, pp. 475–485, 1998.
    4. E. Bullinger, A. Ilchmann, and F. Allgöwer, “A Simple Adaptive Observer for Nonlinear Systems,” in Nonlinear control systems design 1998 : a proceedings volume from  the 4th IFAC Symposium, Enschede, The Netherlands, vol. 2, H. J. C. Huijberts, H. Nijmeijer, A. J. van der Schaft, and J. M. A. Scherpen, Eds. Oxford, UK: Pergamon, 1998, pp. 781–786.
    5. H. Chen and F. Allgöwer, “Nonlinear model predictive control schemes with guaranteed stability,” in Nonlinear Model Based Process Control, R. Berber and C. Kravaris, Eds. Dordrecht, The Netherlands: Kluwer Academic Publishers, 1998, pp. 465–494.
    6. H. Chen, C. W. Scherer, and F. Allgöwer, “A robust model predictive control scheme for constrained linear systems,” in Proc. 5th IFAC Symp. Dynamics and Control of Process Systems (DYCOPS), Corfu, Greece, 1998, pp. 60–65.
    7. R. K. Pearson, P. H. Menold, and F. Allgöwer, “Practically-motivated input sequences for nonlinear model identification,” in Proc. American Control Conf. (ACC), Philadelphia, PA, USA, 1998, pp. 1235–1239.
  23. 1997

    1. P. H. Menold, R. K. Pearson, and F. Allgöwer, “Nonlinear structure identification of chemical processes,” Comp. & Chem. Eng., vol. 21, pp. 137–142, 1997.
    2. P. H. Menold, R. K. Pearson, and F. Allgöwer, “Nonlinear structure identification of chemical processes,” Comp.\ & Chem.\ Eng., vol. 21, pp. 137–142, 1997.
    3. E. Bullinger and F. Allgöwer, “An Adaptive High-Gain Observer for Nonlinear Systems,” in Proc. 36th IEEE Conf. Decision and Control (CDC), San Diego, CA, USA, 1997, pp. 4348--4353.
    4. H. Chen and F. Allgöwer, “A quasi-infinite horizon nonlinear predictive control scheme for  stable systems: Application to a CSTR,” in Proc. IFAC Int. Symp. Advanced Control of Chemical Processes (ADCHEM), Banff, Canada, 1997, pp. 471–476.
    5. H. Chen and F. Allgöwer, “Quasi-infinite horizon nonlinear predictive control,” in Workshop on Control of Nonlinear and Uncertain Systems (COSY), London, UK, 1997, pp. 52–57.
    6. H. Chen and F. Allgöwer, “A quasi-infinite horizon nonlinear model predictive control scheme  with guaranteed stability,” in Proc. European Control Conf. (ECC), 1997.
    7. H. Chen, C. W. Scherer, and F. Allgöwer, “A game theoretic approach to nonlinear robust receding horizon control  of constrained systems,” in Proc. American Control Conf. (ACC), Albuquerque, NM, USA, 1997, pp. 3073–3077.
    8. P. H. Menold, F. Allgöwer, and R. K. Pearson, “On simple representation of distillation dynamics,” in Proc. 1st European Congress on Chemical Engineering (ECCE), Florence, Italy, 1997, pp. 1363–1366.
    9. R. K. Pearson, F. Allgöwer, and P. H. Menold, “Stochastic suitability measures for nonlinear structure identification,” in Proc. European Control Conf. (ECC), Bruessels, Belgium, 1997.
    10. E. Bullinger and F. Allgöwer, “Ein adaptiver high-gain Beobachter für nichtlineare Systeme.” 1997.
    11. P. H. Menold, “Nonlinear structure identification of chemical processes.” 1997.
    12. P. H. Menold, “Struktureignungsmasse zur nichtlinearen Systemidentifikation.” 1997.
    13. P. H. Menold and F. Allgöwer, “Struktureignungsmaße zur nichtlinearen Systemidentifikation.” 1997.
  24. 1996

    1. R. Bacher and E. Bullinger, “Application of non-stationary iterative methods to an exact Newton-Raphson  solution process for power flow equations,” in 12th Power Systems Computation Conference, Dresden, Germany, 1996, pp. 453--459.
    2. H. Chen and F. Allgöwer, “A quasi-infinite horizon predictive control scheme for constrained  nonlinear systems,” in Proc. 16th Chinese Control Conf., Qindao, China, 1996, pp. 309–316.
  25. 1995

    1. H. Chen, A. Kremling, and F. Allgöwer, “Nonlinear predictive control of a benchmark CSTR,” in Proc. European Control Conf. (ECC), Rome, Italy, 1995, pp. 3247–3252.
    2. H. Chen and F. Allgöwer, “Maximal yield control of a nonlinear chemical reactor,” in Proc. 1st IFAC Youth Automation Conf. (YAC), Beijing, China, 1995, pp. 764–769.
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