Publications

IST studies and research papers

Here, you can find a list of all publications of the IST.

Publication list

  1. 2021

    1. V. Avrutin, F. Bastian, and Zh. T. Zhusubaliyev, “A geometric approach to bubbling,” Physica D, vol. 417, p. 132808, 2021, doi: 10.1016/j.physd.2020.132808.
    2. V. Avrutin, Zh. T. Zhusubaliyev, and F. Bastian, “Transformations of Closed Invariant Curves and Closed-Invariant-Curve-Like Chaotic Attractors in Piecewise Smooth Systems,” Int. J. Bifurcat. Chaos, vol. 31, no. 3, Art. no. 3, 2021, doi: 10.1142/S0218127421300093.
    3. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-driven model predictive control with stability and robustness guarantees,” IEEE Trans. Automat. Control, vol. 66, no. 4, Art. no. 4, 2021, doi: 10.1109/TAC.2020.3000182.
    4. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-driven model predictive control: closed-loop guarantees and experimental results,” at-Automatisierungstechnik, vol. 69, no. 7, Art. no. 7, 2021, doi: 10.1515/auto-2021-0024.
    5. M. Gharbi, F. Bayer, and C. Ebenbauer, “Proximity Moving Horizon Estimation for Discrete-Time Nonlinear Systems,” IEEE Control Systems Lett., vol. 5, no. 6, Art. no. 6, 2021, doi: 10.1109/LCSYS.2020.3046377.
    6. R. Henriquez-Auba, P. Hidalgo-Gonzalez, P. Pauli, D. Kalathil, D. S. Callaway, and K. Poolla, “Sharing economy and optimal investment decisions for distributed solar generation,” Applied Energy, vol. 294, p. 117029, 2021, doi: 10.1016/j.apenergy.2021.117029.
    7. M. Hertneck, S. Linsenmayer, and F. Allgöwer, “Efficient stability analysis approaches for nonlinear  weakly-hard real-time control systems,” Automatica, vol. 133, p. 109868, 2021, doi: https://doi.org/10.1016/j.automatica.2021.109868.
    8. V. Klingel, J. Kirch, T. Ullrich, S. Weirich, A. Jeltsch, and N. E. Radde, “Model-based robustness and bistability analysis for methylation-based, epigenetic memory systems,” The FEBS Journal, vol. 288, no. 19, Art. no. 19, 2021, doi: 10.1111/febs.15838.
    9. A. Koch, J. M. Montenbruck, and F. Allgöwer, “Sampling Strategies for Data-Driven Inference of Input-Output System Properties,” IEEE Trans. Automat. Control, vol. 66, pp. 1144–1159, 2021, doi: 10.1109/TAC.2020.2994894.
    10. S. Kryzhevich, V. Avrutin, N. Begun, D. Rachinskii, and K. Tajbakhs, “Dynamics of Systems with a Discontinuous Hysteresis Operator and Interval Translation Maps,” Axioms (Special Issue ``Topological Theory of Dynamical Systems’’), vol. 10, no. 2, Art. no. 2, 2021, doi: 10.3390/axioms10020080.
    11. S. Linsenmayer, B. W. Carabelli, S. Wildhagen, K. Rothermel, and F. Allgöwer, “Controller and Triggering Mechanism Co-Design for Control over Time-Slotted Networks,” IEEE Trans.\ Control of Network Systems, vol. 8, no. 1, Art. no. 1, 2021, doi: 10.1109/TCNS.2020.3024316.
    12. S. Linsenmayer, M. Hertneck, and F. Allgöwer, “Linear Weakly Hard Real-Time Control Systems: Time- and Event-Triggered Stabilization,” IEEE Trans.\ Automat.\ Control, vol. 66, no. 4, Art. no. 4, 2021, doi: 10.1109/TAC.2020.3000981.
    13. T. Martin and F. Allgöwer, “Dissipativity verification with guarantees for polynomial systems from noisy input-state data,” IEEE Control Systems Lett., vol. 5, no. 4, Art. no. 4, 2021, doi: 10.1109/LCSYS.2020.3037842.
    14. P. Pauli, A. Koch, J. Berberich, P. Kohler, and F. Allgöwer, “Training Robust Neural Networks using Lipschitz Bounds,” IEEE Control Systems Lett., vol. 6, pp. 121–126, 2021, doi: 10.1109/LCSYS.2021.3050444.
    15. V. Wagner and N. Radde, “SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems,” Mathematics, vol. 9, p. 1074, 2021, doi: 10.3390/math9101074.
    16. P. Wenzelburger and F. Allgöwer, “Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0,” Applied Sciences, vol. 11, no. 17, Art. no. 17, 2021, doi: 10.3390/app11178145.
    17. Zh. T. Zhusubaliyev, V. Avrutin, V. G. Rubanov, and D. A. Bushuev, “Complex dynamics of a vibration machine caused by a relay feedback control,” Physica D, vol. 420, p. 132870, 2021, doi: 10.1016/j.physd.2021.132870.
    18. N. Wieler, J. Berberich, A. Koch, and F. Allgöwer, “Data-driven controller design via finite-horizon dissipativity,” in Proc. 3rd Learning for Dynamics and Control Conf. (L4DC), Zürich, Switzerland, 2021, vol. 144, pp. 287–298.
  2. 2020

    1. S. Adam et al., “DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation,” Nat. Communications, vol. 11, no. 1, Art. no. 1, 2020, doi: 10.1038/s41467-020-17531-8.
    2. V. Avrutin and Zh. T. Zhusubaliyev, “Piecewise-linear map for studying border-collision phenomena in DC/AC converters,” Int. J. Bifurcat. Chaos, vol. 30, no. 7, Art. no. 7, 2020, doi: 10.1142/S0218127420300153.
    3. V. Avrutin, Zh. T. Zhusubaliyev, and A. El Aroudi, “Non-visible Transformations of Chaotic Attractors due to their Ultra Low Density in AC-DC Power Factor Correction Converters,” Nonlinear Dynamics, vol. 102, pp. 2905–2924, 2020, doi: 10.1007/s11071-020-06077-5.
    4. V. Avrutin, Z. Zh.T., D. Suissa, and E. A. A., “Non-observable chaos in piecewise smooth systems,” Nonlinear Dynamics, vol. 99, no. 3, Art. no. 3, 2020, doi: 10.1007/s11071-019-05406-7.
    5. J. Berberich, J. Köhler, F. Allgöwer, and M. A. Müller, “Dissipativity properties in constrained optimal control: A computational approach,” Automatica, vol. 114, p. 108840, 2020, doi: 10.1016/j.automatica.2020.108840.
    6. M. J. Colbrook, Z. I. Botev, K. Kuritz, and S. MacNamara, “Kernel density estimation with linked boundary conditions,” Studies in Applied Mathematics, vol. 145, no. 3, Art. no. 3, 2020.
    7. M. Gharbi and C. Ebenbauer, “A proximity moving horizon estimator for a class of nonlinear systems,” Int. J. Adapt. Control Signal Process., vol. 34, no. 6, Art. no. 6, 2020, doi: 10.1002/acs.3092.
    8. D. Imig, N. Pollak, F. Allgöwer, and M. Rehm, “Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis,” PLoS Computational Biology, vol. 16, no. 6, Art. no. 6, 2020.
    9. K. Kuritz, D. Stöhr, D. S. Maichl, N. Pollak, M. Rehm, and F. Allgöwer, “Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities,” Scientific Reports, vol. 10, no. 1, Art. no. 1, 2020, doi: 10.1038/s41598-020-60400-z.
    10. J. Köhler, L. Schwenkel, A. Koch, J. Berberich, P. Pauli, and F. Allgöwer, “Robust and optimal predictive control of the COVID-19 outbreak,” Annual reviews in Control, 2020.
    11. J. Köhler, R. Soloperto, M. A. Müller, and F. Allgöwer, “A computationally efficient robust model predictive control framework for uncertain nonlinear systems,” IEEE Trans. Automat. Control, 2020.
    12. J. Köhler, M. A. Müller, and F. Allgöwer, “A nonlinear model predictive control framework using reference generic terminal ingredients,” IEEE Trans. Automat. Control, vol. 65, no. 8, Art. no. 8, 2020.
    13. J. Köhler, P. Kötting, R. Soloperto, F. Allgöwer, and M. A. Müller, “A robust adaptive model predictive control framework for nonlinear uncertain systems,” Int. J. Robust and Nonlinear Control, pp. 1–25, 2020.
    14. J. Köhler, M. A. Müller, and F. Allgöwer, “A nonlinear tracking model predictive control scheme for unreachable dynamic target signals,” Automatica, vol. 118, p. 109030, 2020.
    15. J. Köhler, M. A. Müller, and F. Allgöwer, “Periodic optimal control of nonlinear constrained systems using economic model predictive control,” J. Proc. Contr., vol. 92, pp. 185–201, 2020.
    16. S. Michalowsky, B. Gharesifard, and C. Ebenbauer, “A Lie bracket approximation approach to distributed optimization over directed graphs,” Automatica, vol. 112, p. 108691, 2020.
    17. S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: an integral quadratic constraint approach,” International Journal of Control, pp. 1–24, 2020.
    18. J. Nubert, J. Köhler, V. Berenz, F. Allgöwer, and S. Trimpe, “Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control,” IEEE Robotics and Automation Letters, vol. 5, no. 2, Art. no. 2, 2020.
    19. L. Schwenkel, M. Gharbi, S. Trimpe, and C. Ebenbauer, “Online learning with stability guarantees: A memory-based warm-starting for real-time MPC,” Automatica, vol. 122, p. 109247, 2020, doi: https://doi.org/10.1016/j.automatica.2020.109247.
    20. D. Simpson, V. Avrutin, and S. Banerjee, “Nordmark map and the problem of large-amplitude chaos in impact oscillators,” Phys. Rev. E, vol. 102, no. 2, Art. no. 2, 2020, doi: 10.1103/PhysRevE.102.022211.
    21. R. Soloperto, J. Köhler, and F. Allgöwer, “Augmenting MPC schemes with active learning: Intuitive tuning and guaranteed performance,” IEEE Control Systems Letters, vol. 4, no. 3, Art. no. 3, 2020.
    22. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Robust constraint satisfaction in data-driven MPC,” in Proc. 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 1260–1267. doi: 10.1109/CDC42340.2020.9303965.
    23. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-driven tracking MPC for changing setpoints,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 971–976. doi: 10.1016/j.ifacol.2020.12.389.
    24. J. Berberich, A. Koch, C. W. Scherer, and F. Allgöwer, “Robust data-driven state-feedback design,” in Proc. American Control Conf. (ACC), Denver, CO, USA, 2020, pp. 1532–1538. doi: 10.23919/ACC45564.2020.9147320.
    25. J. Berberich and F. Allgöwer, “A trajectory-based framework for data-driven system analysis and control,” in Proc. European Control Conf. (ECC), Saint Petersburg, Russia, 2020, pp. 1365–1370. doi: 10.23919/ECC51009.2020.9143608.
    26. A. Camisa, P. N. Köhler, M. A. Müller, G. Notarstefano, and F. Allgöwer, “A distributed optimization algorithm for Nash bargaining in multi-agent systems,” Berlin, Germany, 2020.
    27. M. Gharbi and C. Ebenbauer, “An iteration scheme with stability guarantees for proximity moving horizon estimation,” in Proc. 19th European Control Conf. (ECC), Saint Petersburg, Russia, 2020, pp. 973–978. doi: 10.23919/ECC51009.2020.9143840.
    28. M. Hertneck, S. Linsenmayer, and F. Allgöwer, “Stability Analysis for Nonlinear Weakly Hard Real-Time Control Systems,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 2632–2637. doi: 10.1016/j.ifacol.2020.12.307.
    29. M. Hertneck and F. Allgöwer, “Exploiting Information for Decentralized Periodic Event-Triggered Control,” in Proc. 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 4999–5004. doi: 10.1109/CDC42340.2020.9304456.
    30. M. Hertneck, S. Linsenmayer, and F. Allgöwer, “Stabilization of Nonlinear Weakly Hard Real-Time Control Systems,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 2632–2637. doi: 10.1016/j.ifacol.2020.12.307.
    31. M. Hertneck, S. Linsenmayer, and F. Allgöwer, “Model-Based Nonlinear Periodic Event-Triggered Control for Continuous-Time Systems with Sampled-Data Prediction,” in Proc. European Control Conf. (ECC), Saint Petersburg, Russia, 2020, pp. 1814–1819.
    32. M. Hirche, P. N. Köhler, M. A. Müller, and F. Allgöwer, “Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems,” in Proc. 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 1248–1253. doi: 10.1109/CDC42340.2020.9303838.
    33. F. Jaumann, S. Wildhagen, and F. Allgöwer, “Saving Tokens in Rollout Control with Token Bucket Specification,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 2662–2669. doi: 10.1016/j.ifacol.2020.12.313.
    34. A. Koch, M. Lorenzen, P. Pauli, and F. Allgöwer, “Facilitating learning progress in a first control course via Matlab apps,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 17356–17361. doi: 10.1016/j.ifacol.2020.12.2086.
    35. A. Koch, J. Berberich, and F. Allgöwer, “Verifying dissipativity properties from noise-corrupted input-state data,” in Proc. 59th IEEE Conf. on Decision and Control (CDC), Jeju, South Korea, 2020, pp. 616–621. doi: 10.1109/CDC42340.2020.9304380.
    36. J. Köhler, M. A. Müller, and F. Allgöwer, “Implicit solutions to constrained nonlinear output regulation using MPC,” in Proc.\ 59th IEEE Conf.\ Decision and Control (CDC), 2020, pp. 4604–4609.
    37. Y. Lian, S. Wildhagen, Y. Jiang, B. Houska, F. Allgöwer, and C. N. Jones, “Resource-Aware Asynchronous Multi-Agent Coordination Via Self-Triggered MPC,” in 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 685–690. doi: 10.1109/CDC42340.2020.9304137.
    38. T. Martin, A. Koch, and F. Allgöwer, “Data-driven surrogate models for LTI systems via saddle-point dynamics,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 971–976. doi: 10.1016/j.ifacol.2020.12.1261.
    39. T. Martin and F. Allgöwer, “Iterative data-driven inference of nonlinearity measures via successive graph approximation,” in Proc. 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 4760–4765. doi: 10.1109/CDC42340.2020.9304285.
    40. E. Müller, P. N. Köhler, K. Y. Pettersen, and F. Allgöwer, “Economic model predictive control for obstacle-aided snake robot locomotion,” Berlin, Germany, 2020.
    41. P. Pauli, A. Koch, and F. Allgöwer, “Smartphone Apps for Learning Progress and Course Revision,” Berlin, Germany, 2020.
    42. D. Persson, A. Koch, and F. Allgöwer, “Probabilistic H2-norm estimation via Gaussian process system identification,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 431–436. doi: 10.1016/j.ifacol.2020.12.211.
    43. M. Rosenfelder, J. Köhler, and F. Allgöwer, “Stability and performance in transient average constrained economic MPC without terminal constraints,” 2020.
    44. H. Schlüter and F. Allgöwer, “A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances,” in Proc.\ 21th IFAC World Congress, Berlin, Germany, 2020, pp. 7130–7135. doi: 10.1016/j.ifacol.2020.12.518.
    45. L. Schwenkel, M. Guo, and M. Bürger, “Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration,” in Proc. 3rd Conf. on Robot Learning (CoRL), Osaka, Japan, 2020, vol. 100, pp. 273–282.
    46. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Dynamic uncertainties in model predictive control: Guaranteed stability for constrained linear systems,” in 59th IEEE Conference on Decision and Control (CDC), 2020, pp. 1235–1241. [Online]. Available: https://doi.org/10.1109/CDC42340.2020.9303819
    47. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Robust Economic Model Predictive Control without Terminal Conditions,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 7097–7104. doi: 10.1016/j.ifacol.2020.12.465.
    48. J. Venkatasubramanian, J. Köhler, J. Berberich, and F. Allgöwer, “Robust dual control based on gain scheduling,” in Proc. 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 2270–2277. doi: 10.1109/CDC42340.2020.9304336.
    49. S. Wildhagen and F. Allgöwer, “Rollout scheduling and control for disturbed systems via tube MPC,” in Proc. 59th IEEE Conf. Decision and Control (CDC), Jeju, South Korea, 2020, pp. 3145–3150. doi: 10.1109/CDC42340.2020.9304512.
    50. S. Wildhagen, C. N. Jones, and F. Allgöwer, “A resource-aware approach to self-triggered model predictive control,” Berlin, Germany, 2020. doi: 10.1016/j.ifacol.2020.12.926.
    51. S. Wildhagen and F. Allgöwer, “Scheduling and control over networks using MPC with time-varying terminal ingredients,” in Proc. American Control Conf. (ACC), Denver, CO, USA, 2020, pp. 1913–1918. doi: 10.23919/ACC45564.2020.9147411.
  3. 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, doi: 10.1016/j.nahs.2019.05.007.
    2. V. Avrutin and Zh. T. Zhusubaliyev, “Nested closed invariant curves,” Int. J. Bifurcat. Chaos, vol. 29, no. 7, Art. no. 7, 2019, doi: 10.1142/S0218127419300179.
    3. 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.
    4. I. Eisenkolb et al., “Modeling of biocatalytic reactions: A workflow for model calibration, selection, and validation using Bayesian statistics,” AIChE Journal, vol. n/a, no. n/a, Art. no. n/a, 2019, doi: 10.1002/aic.16866.
    5. E. Geissen, J. Hasenauer, and N. Radde, “Inference of finite mixture models and the effect of binning,” Stat. Appl. Genet. Mol. Biol., vol. 18, no. 4, Art. no. 4, 2019.
    6. 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.
    7. 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, doi: 10.1016/j.automatica.2019.04.039.
    8. 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, doi: https://doi.org/10.1007/s10010-019-00333-w.
    9. A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input-output data,” IEEE Control Systems Lett., vol. 3, pp. 709–714, 2019, doi: 10.1109/LCSYS.2019.2917162.
    10. S. Wildhagen, M. A. Müller, and F. Allgöwer, “Predictive Control over a Dynamical Token Bucket Network,” IEEE Control Systems Lett., vol. 3, no. 4, Art. no. 4, 2019, doi: 10.1109/LCSYS.2019.2919264.
    11. V. Avrutin, L. Gardini, I. Sushko, and F. Tramontana, Continuous and Discontinuous Piecewise-Smooth One-dimensional Maps: Invariant Sets and Bifurcation Structures, vol. 95. World Scientific, 2019. doi: 10.1142/8285.
    12. M. Belopolskaya, V. Avrutin, A. Dmitriev, and A. Yakovlev, “Effects of direct acting antiviral drugs on a fibrosis in patients with cirrhotic stage of hepatitis C,” Vilnius, Lithuania, 2019.
    13. J. Berberich, M. Sznaier, and F. Allgöwer, “Signal estimation and system identification with nonlinear dynamic sensors,” in 3rd IEEE Conf. Control Technology and Applications (CCTA), Hong Kong, China, 2019, pp. 505–510. doi: 10.1109/CCTA.2019.8920592.
    14. J. Falk, F. Dürr, S. Linsenmayer, S. Wildhagen, B. W. Carabelli, and K. Rothermel, “Optimal routing and scheduling of complemental flows in converged networks,” in Proc. 27th Int. Conf. Real-Time Networks and Systems (RTNS), Toulouse, France, 2019, pp. 154–164. doi: 10.1145/3356401.3356415.
    15. M. Gharbi and C. Ebenbauer, “A proximity moving horizon estimator based on Bregman distances and relaxed barrier functions,” in Proc. 18th European Control Conf. (ECC), Napoli, Italy, 2019, pp. 1790–1795. doi: 10.23919/ECC.2019.8795714.
    16. M. Gharbi and C. Ebenbauer, “Proximity moving horizon estimation for linear time-varying systems and a Bayesian filtering view,” in Proc. 58th IEEE Conf. on Decision and Control (CDC), Nice, France, 2019, pp. 3208–3213. doi: 10.1109/CDC40024.2019.9029264.
    17. W. Halter, S. Michalowsky, and F. Allgöwer, “Extremum seeking for optimal enzyme production under cellular fitness constraints,” Neapel, Italien, 2019.
    18. M. Hertneck, S. Linsenmayer, and F. Allgöwer, “Nonlinear Dynamic Periodic Event-Triggered Control with Robustness to Packet Loss Based on Non-Monotonic Lyapunov Functions,” in Proc. 58th IEEE Conf. Decision and Control (CDC), Nice, France, 2019, pp. 1680–1685. doi: 10.1109/CDC40024.2019.9029770.
    19. 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.
    20. J. Köhler, E. Andina, R. Soloperto, M. A. Müller, and F. Allgöwer, “Linear robust adaptive model predictive control: Computational complexity and conservatism,” in Proc. 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019, pp. 1383–1388.
    21. P. N. Köhler, M. A. Müller, and F. Allgöwer, “Graph topology and subsystem centrality in approximately dissipative system interconnections,” in Proc. 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019, pp. 7441–7447.
    22. 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.
    23. S. Linsenmayer, M. A. Müller, H. Ishii, and F. Allgöwer, “Event-based Containability for Linear Systems with Arbitrarily Small Bit Rates,” in Proc. 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), Chicago, IL, USA, 2019, pp. 31–36. doi: 10.1016/j.ifacol.2019.12.138.
    24. 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. doi: 10.1109/CCNC.2019.8651811.
    25. T. Martin and F. Allgöwer, “Nonlinearity Measures for Data-Driven System Analysis and Control,” in Proc. 58th IEEE Conf. Decision and Control (CDC), Nice, France, 2019, pp. 3605–3610. doi: 10.1109/CDC40024.2019.9029804.
    26. 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, PA, USA, 2019, pp. 1020–1026. doi: 10.23919/ACC.2019.8814305.
    27. M. Nonhoff, P. N. Köhler, and F. Allgöwer, “Economic model predictive control for snake robot locomotion,” Nice, France, 2019.
    28. A. Romer, S. Trimpe, and F. Allgöwer, “Data-driven inference of passivity properties via Gaussian process optimization,” in Proc. European Control Conf. (ECC), Naples, Italy, 2019, pp. 29–35. doi: 10.23919/ECC.2019.8795728.
    29. D. Simpson, V. Avrutin, and S. Banerjee, “Nordmark map and the problem of large-amplitude chaos in an impact oscillator,” Lodz, Poland, 2019.
    30. R. Soloperto, J. Köhler, M. A. Müller, and F. Allgöwer, “Collision avoidance for uncertain nonlinear systems and moving obstacles using robust Model Predictive Control,” Naples, Italy, 2019.
    31. R. Soloperto, J. Köhler, M. A. Müller, and F. Allgöwer, “Dual Adaptive MPC for output tracking of linear systems,” Nice, France, 2019.
    32. P. Wenzelburger and F. Allgöwer, “A Petri Net Modeling Framework for the Control of Flexible Manufacturing Systems,” in Proc. 9th IFAC Conf. Manufacturing Modeling, Management, and Control (MIM), Berlin, Germany, 2019, pp. 492–498. doi: 10.1016/j.ifacol.2019.11.111.
    33. P. Wenzelburger and F. Allgöwer, “A Novel Optimal Online Scheduling Scheme for Flexible Manufacturing Systems,” in Proc. 13th IFAC Workshop on Intelligent Manufacturing Systems (IMS), Oshawa, Canada, 2019, pp. 1–6. doi: 10.1016/j.ifacol.2019.10.002.
    34. S. Wildhagen, M. A. Müller, and F. Allgöwer, “Economic MPC using a Cyclic Horizon with Application to Networked Control Systems,” in Proc. 11th IFAC Symp. Nonlinear Control Systems (NOLCOS), Vienna, Austria, 2019, pp. 796–801. doi: 10.1016/j.ifacol.2019.12.011.
  4. 2018

    1. R. Apweiler et al., “Whither Systems Medicine?,” Exp. Mol. Med., vol. 50, p. 453, 2018.
    2. F. A. Bayer, M. A. Müller, and F. Allgöwer, “On optimal system operation in robust economic MPC,” Automatica, vol. 88, pp. 98–106, 2018, doi: https://doi.org/10.1016/j.automatica.2017.11.007.
    3. 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, vol. 9, p. 91, 2018, doi: 10.3389/fendo.2018.00091.
    4. J. Berberich, J. Köhler, F. Allgöwer, and M. A. Müller, “Indefinite Linear Quadratic Optimal Control: Strict Dissipativity and Turnpike Properties,” IEEE Control Systems Lett., vol. 2, no. 3, Art. no. 3, 2018, doi: 10.1109/LCSYS.2018.2842142.
    5. 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, Art. no. 9, 2018.
    6. 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.
    7. 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, Art. no. 6, 2018, doi: https://doi.org/10.1371/journal.pone.0198203.
    8. J. Feiling, A. Zeller, and C. Ebenbauer, “Derivative-Free Optimization Algorithms Based on Non-Commutative Maps,” IEEE Control Systems Letters, vol. 2, no. 4, Art. no. 4, 2018, doi: 10.1109/LCSYS.2018.2849596.
    9. 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.
    10. M. Hertneck, J. Köhler, S. Trimpe, and F. Allgöwer, “Learning an approximate model predictive controller with guarantees,” IEEE Control Systems Lett., vol. 2, no. 3, Art. no. 3, 2018, doi: 10.1109/LCSYS.2018.2843682.
    11. 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, Art. no. 19, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.09.028.
    12. 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, Art. no. 37, 2018, doi: 10.1074/jbc.RA118.003787.
    13. K. Kuritz, S. Zeng, and F. Allgöwer, “Ensemble Controllability of Cellular Oscillators,” IEEE Control Systems Letters, vol. 3, no. 2, Art. no. 2, 2018, doi: 10.1109/LCSYS.2018.2870967.
    14. 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, Art. no. 19, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.09.034.
    15. J. Köhler, M. A. Müller, and F. Allgöwer, “Nonlinear reference tracking: An economic model predictive control perspective,” IEEE Trans. Automat. Control, vol. 64, pp. 254–269, 2018.
    16. J. Köhler, M. A. Müller, and F. Allgöwer, “On periodic dissipativity notions in economic model predictive control,” IEEE Control Systems Letters, vol. 2, no. 3, Art. no. 3, 2018.
    17. 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, doi: https://doi.org/10.1016/j.automatica.2018.07.001.
    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, Art. no. 1112, 2018, doi: 10.1038/s41419-018-1160-2.
    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 Lett., vol. 2, no. 4, Art. no. 4, 2018, doi: 10.1109/lcsys.2018.2847449.
    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, Art. no. 4, 2018, doi: 10.1109/TCNS.2017.2733959.
    21. A. Medvedev, P. Mattsson, Zh. T. Zhusubaliyev, and V. Avrutin, “Nonlinear dynamics and entrainment in a continuously forced pulse-modulated model of testosterone regulation,” Nonlinear Dynamics, vol. 94, no. 2, Art. no. 2, 2018, doi: 10.1007/s11071-018-4416-6.
    22. D. Paul and N. Radde, “The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs,” J. Theor. Biol., vol. 455, pp. 86–96, 2018.
    23. C. Thomaseth, D. Fey, T. Santra, O. S. Rukhlenko, N. E. Radde, and B. N. Kholodenko, “Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction,” Scientific reports, vol. 8, no. 1, Art. no. 1, 2018.
    24. Zh. T. Zhusubaliyev, V. Avrutin, V. Rubanov, D. Bushuev, D. Titov, and O. Yanochkina, “Persistence Border Collisions in a Vibrating System Excited by an Unbalanced Motor with a Relay Control,” AIP Conf. Proc., vol. 1959, p. 080022, 2018, doi: 10.1063/1.5034739.
    25. 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. doi: 10.1007/978-3-319-67068-3_1.
    26. V. Avrutin, Zh. T. Zhusubaliyev, and A. El Aroudi, “Non-Observable Chaos in Power Converters,” Tarragona, Spain, 2018.
    27. M. Gharbi and C. Ebenbauer, “A Proximity Approach to Linear Moving Horizon Estimation,” in Proc. 6th IFAC Conf. Nonlinear Model Predictive Control    (NMPC), Madison, USA, 2018, pp. 649–655. doi: 10.1016/j.ifacol.2018.11.033.
    28. W. Halter, F. Allgöwer, R. M. Murray, and A. Gyorgy, “Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts,” Miami Beach, USA, 2018.
    29. 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.
    30. 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.
    31. 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, pp. 728–734.
    32. 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.
    33. 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, pp. 1355–1360.
    34. 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.
    35. 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. doi: 10.23919/ECC.2018.8550568.
    36. 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.
    37. 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 (ACC), Milwaukee, Wisconsin, USA, 2018, pp. 6094–6100. doi: 10.23919/ACC.2018.8431399.
    38. 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 (ROCOND), Florianópolis, Brazil, 2018, pp. 586–591. doi: 10.1016/j.ifacol.2018.11.139.
    39. 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, FL, USA, 2018, pp. 21–26. doi: 10.1109/CDC.2018.8619442.
    40. R. Soloperto, M. A. Müller, and F. Allgöwer, “Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty,” Madison, Wisconsin, 2018.
    41. Zh. T. Zhusubaliyev, V. Avrutin, V. Rubanov, D. Bushuev, D. Titov, and O. Yanochkina, “Persistence border collisions in a vibration system with a relay control,” Tarragona, Spain, 2018.
  5. 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, doi: 10.1016/j.physd.2016.12.008.
    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, Art. no. 14, 2017, doi: 10.1142/S0218127416300408.
    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, doi: 10.1016/j.chaos.2017.08.003.
    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, Art. no. 3, 2017.
    6. G. Goebel and F. Allgöwer, “New results on semi-explicit and almost explicit MPC algorithms,” at-Automatisierungstechnik, vol. 65, no. 4, Art. no. 4, 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, Art. no. 1, 2017, doi: 10.1186/s12918-017-0392-6.
    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, doi: 10.1016/j.jtbi.2016.11.024.
    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, M. A. Müller, and F. Allgöwer, “Stochastic Model Predictive Control without Terminal Constraints,” Int. J. Robust and Nonlinear Control, 2017, doi: 10.1002/rnc.3912.
    14. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Stochastic MPC with Offline Uncertainty Sampling,” Automatica, vol. 81, pp. 176–183, 2017, doi: https://doi.org/10.1016/j.automatica.2017.03.031.
    15. 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, Art. no. 7, 2017, doi: 10.1109/TAC.2016.2625048.
    16. J. M. Montenbruck and S. Zeng, “Collinear Dynamical Systems,” Syst. Contr. Lett., vol. 105, pp. 34–43, 2017.
    17. J. M. Montenbruck, M. Arcak, and F. Allgöwer, “An Input-Output Framework for Submanifold Stabilization,” IEEE Trans. Automat. Control, vol. 62, no. 10, Art. no. 10, 2017.
    18. J. M. Montenbruck, D. Zelazo, and F. Allgöwer, “Fekete Points, Formation Control, and the Balancing Problem,” IEEE Trans. Automat. Control, vol. 62, no. 10, Art. no. 10, 2017.
    19. 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, Art. no. 2, 2017.
    20. M. A. Müller and K. Worthmann, “Quadratic costs do not always work in MPC,” Automatica, vol. 82, pp. 269–277, 2017.
    21. M. A. Müller, “Nonlinear moving horizon estimation in the presence of bounded disturbances,” Automatica, vol. 79, pp. 306–314, 2017.
    22. A. Panchuk, I. Sushko, and V. Avrutin, “Bifurcation Structures in a Bimodal Piecewise Linear Map,” Front. Appl. Math. Stat., vol. 3, no. 7, Art. no. 7, 2017, doi: 10.3389/fams.2017.00007.
    23. C. Thomaseth, K. Kuritz, F. Allgoewer, and R. N., “The circuit-breaking algorithm for monotone systems,” Mathematical Biosciences, vol. 284, pp. 80–91, 2017.
    24. S. Zeng and F. Allgöwer, “Structured optimal feedback in multi-agent systems: A static output feedback perspective,” Automatica, vol. 76, pp. 214–221, 2017, doi: 10.1016/j.automatica.2016.10.021.
    25. A. Zuyev and V. Grushkovskaya, “Motion planning for control-affine systems satisfying low-order controllability conditions,” Int. J. Control, vol. 90, no. 11, Art. no. 11, 2017, doi: 10.1080/00207179.2016.1257157.
    26. 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.
    27. V. Avrutin, Zh. T. Zhusubaliyev, and E. Mosekilde, “Low-dimensional piecewise smooth maps with an unpredictable number of switching manifolds,” Budapest, Hungary, 2017.
    28. B. W. Carabelli, R. Blind, F. Dürr, and K. Rothermel, “State-dependent priority scheduling for networked control systems,” 2017.
    29. 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. doi: 10.1016/j.ifacol.2017.08.2456.
    30. 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. on Decision and Control (CDC), Melbourne, Victoria, Australia, 2017, pp. 2188–2194. doi: 10.1109/CDC.2017.8263969.
    31. 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, pp. 5522–5528. doi: 10.1016/j.ifacol.2017.08.1093.
    32. W. Halter, Z. A. Tuza, and F. Allgöwer, “Signal differentiation with genetic networks,” Toulouse, France, 2017.
    33. W. Halter, J. M. Montenbruck, and F. Allgöwer, “Systems with integral resource consumption,” Melbourne, Australia, 2017.
    34. J. Köhler, M. Manderla, and F. Malchow, “Embedded Model Predictive Direct Switching Control for High Performance Electrical Drives - A Quantitative Comparison,” in Proc. 20th IFAC World Congress, 2017, pp. 11871–11876.
    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. 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. 20th IFAC World Congress, Toulouse, France, 2017, pp. 7875–7880. doi: 10.1016/j.ifacol.2017.08.742.
    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. doi: 10.1109/CDC.2017.8264364.
    40. M. Lorenzen, F. Allgöwer, and M. Cannon, “Adaptive Model Predictive Control with Robust Constraint Satisfaction,” in Proc. 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. doi: 10.1109/CDC.2017.8264623.
    47. A. Romer, J. M. Montenbruck, and F. Allgöwer, “Determining dissipation inequalities from input-output samples,” in Proc. 20th IFAC World Congress, Toulouse, France, 2017, pp. 7789–7794. doi: 10.1016/j.ifacol.2017.08.1053.
    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, pp. 10476–10481. doi: 10.1016/j.ifacol.2017.08.1979.
  6. 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, Art. no. 1, 2016, doi: 10.1088/1742-6596/692/1/012003.
    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, doi: 10.1016/j.physd.2016.02.011.
    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, Art. no. 7, 2016, doi: 10.15406/ghoa.2016.05.00170.
    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. E.-M. Geissen, J. Hasenauer, S. Heinrich, S. Hauf, F. J. Theis, and N. E. Radde, “MEMO: multi-experiment mixture model analysis of censored data,” Bioinformatics, vol. 32, no. 16, Art. no. 16, 2016.
    7. 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, Art. no. 1, 2016, doi: 10.1007/s11071-016-2909-8.
    8. V. Grushkovskaya, “Asymptotic behavior of solutions of nonlinear systems with multiple imaginary eigenvalues,” PAMM, vol. 16, no. 1, Art. no. 1, 2016, doi: 10.1002/pamm.201610124.
    9. L. Grüne and M. A. Müller, “On the relation between strict dissipativity and turnpike properties,” System & Control Letters, vol. 90, pp. 45–53, 2016.
    10. 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, Art. no. 3, 2016.
    11. K. D. Listmann, P. Wenzelburger, and F. Allgöwer, “Industrie 4.0 - (R)evolution ohne Regelungstechnik?,” at-Automatisierungstechnik, vol. 64, no. 7, Art. no. 7, 2016, doi: 10.1515/auto-2016-0039.
    12. 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, Art. no. 7, 2016, doi: 10.11499/sicejl.55.555.
    13. J. M. Montenbruck and F. Allgöwer, “Asymptotic Stabilization of Submanifolds Embedded in Riemannian Manifolds,” Automatica, vol. 74, pp. 349–359, 2016.
    14. 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, Art. no. 2, 2016.
    15. M. A. Müller and L. Grüne, “Economic model predictive control without terminal constraints for optimal periodic behavior,” Automatica, vol. 70, pp. 128–139, 2016.
    16. N. Radde and M.-T. Hütt, “The Physics behind Systems Biology,” Eur. Phys. J. Nonlin. Biomed. Phys., vol. 4, no. 1, Art. no. 1, 2016.
    17. 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.
    18. 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.
    19. I. Sushko, L. Gardini, and V. Avrutin, “Nonsmooth One-dimensional Maps: Some Basic Concepts and Definitions,” J. Differ. Equations Appl., vol. 22, no. 12, Art. no. 12, 2016, doi: 10.1080/10236198.2016.1248426.
    20. J. Wu and F. Allgöwer, “Verteilte Zustandsschätzung zur Ausgangsregulierung von verteilten Systemen mit gekoppelten Messgrößen,” at-Automatisierungstechnik, vol. 64, no. 8, Art. no. 8, 2016.
    21. S. Zeng and F. Allgöwer, “A moment-based approach to ensemble controllability of linear systems,” Syst. Contr. Lett., vol. 98, pp. 49–56, 2016, doi: 10.1016/j.sysconle.2016.09.020.
    22. S. Zeng, S. Waldherr, C. Ebenbauer, and F. Allgöwer, “Ensemble Observability of Linear Systems,” IEEE Trans. Automat. Control, vol. 61, no. 6, Art. no. 6, 2016.
    23. 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, doi: 10.1016/j.cpc.2016.02.020.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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. doi: 10.1016/j.ifacol.2016.10.382.
    37. 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.
    38. J. Kim, H. Shim, and J. Wu, “Distributed optimal kalman-bucy filter with guaranteed stability,” Las Vegas, NV, 2016.
    39. 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.
    40. 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.
    41. 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.
    42. 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. doi: 10.1109/CDC.2016.7798319.
    43. 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.
    44. 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.
    45. J. M. Montenbruck and F. Allgöwer, “Input-Output Control of Composite Systems,” 2016.
    46. J. M. Montenbruck and F. Allgöwer, “Some Problems Arising in Controller Design from Big Data via Input-Output Methods,” 2016.
    47. J. M. Montenbruck, S. Zeng, and F. Allgöwer, “On the Observability Properties of Systems with Rolling Shutter,” 2016.
    48. J. M. Montenbruck and F. Allgöwer, “Persistence of Excitation and the Feedback Theorem for Passive Systems,” 2016.
    49. M. A. Müller, “Nonlinear moving horizon estimation for systems with bounded disturbances,” in Proc. American Control Conf. (ACC), 2016, pp. 883–888.
    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,” Magdeburg, Germany, 2016.
    52. C. Thomaseth and N. Radde, “Normalization of Western blot data affects the statistics of estimators,” in Proc. 6th Foundations of Systems Biology in Engineering (FOSBE), 2016, vol. 26, pp. 56–62.
    53. 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. doi: 10.1016/j.ifacol.2016.12.125.
    54. 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), Tokyo, Japan, 2016, pp. 321–326. doi: 10.1016/j.ifacol.2016.10.417.
    55. 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, pp. 174–179. doi: 10.1109/AUCC.2016.7868183.
    56. 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. doi: 10.1109/CDC.2016.7798874.
    57. 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.
    58. 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.
    59. S. Zeng, H. Ishii, and F. Allgöwer, “State estimation of interconnected ensembles with anonymized outputs,” Tokyo, Japan, 2016.
    60. 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. doi: 10.1109/ecc.2016.7810346.
    61. 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.
    62. 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.
  7. 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, Art. no. 1–3, 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, Art. no. 11, 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, Art. no. 3, 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, Art. no. 15, 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, Art. no. 2, 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, Art. no. 11, 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, Art. no. 7, 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, Art. no. 6, 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, Art. no. 3, 2015, doi: 10.1142/S0218127415300062.
    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 & 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, Art. no. 1, 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. doi: 10.1109/ACC.2015.7171809.
    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, pp. 138–143. doi: 10.1016/j.ifacol.2015.10.320.
    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. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. S. Schuler, “Controller and Network Design Exploiting System Structure,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2015.
    56. 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.
  8. 2014

    1. 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.
    2. 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, Art. no. 8, 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, doi: 10.1016/j.matcom.2013.06.001.
    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, doi: 10.1016/j.matcom.2012.07.019.
    5. F. Bayer, M. A. Müller, and F. Allgöwer, “Tube-based Robust Economic Model Predictive Control,” J. Proc. Contr., vol. 24, no. 8, Art. no. 8, 2014.
    6. 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, Art. no. 1, 2014.
    7. 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, Art. no. 2, 2014.
    8. 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. 345795, 2014.
    9. 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, Art. no. 2, 2014.
    10. S. Heinrich et al., “Determinants of robustness in spindle assembly checkpoint signalling,” Nat. Cell. Biol., vol. 15, no. 11, Art. no. 11, 2014.
    11. A. Kramer, V. Stathopoulus, M. Girolami, and N. Radde, “MCMCCLIB: An advanced MCMC sampling package for ode models.,” Bioinformatics, vol. 30, no. 20, Art. no. 20, 2014.
    12. 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.
    13. 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, Art. no. 11, 2014.
    14. 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, doi: http://dx.doi.org/10.1016/j.ijepes.2014.04.050.
    15. 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, Art. no. 8, 2014.
    16. M. A. Müller, D. Angeli, and F. Allgöwer, “Transient average constraints in economic model predictive control,” Automatica, vol. 50, no. 11, Art. no. 11, 2014.
    17. 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, Art. no. 12, 2014.
    18. 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, Art. no. 2, 2014.
    19. 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. 569296, 2014.
    20. D. Radi, L. Gardini, and V. Avrutin, “The Role of Constraints in a Segregation Model: The Symmetric Case,” Chaos, Solitons & Fractals, vol. 66, pp. 103–119, 2014.
    21. 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, Art. no. 2, 2014.
    22. 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, Art. no. 2, 2014.
    23. 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, Art. no. 1, 2014, doi: 10.1137/12086652X.
    24. 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, Art. no. 9, 2014.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. M. Lorenzen and M.-A. Belabbas, “Distributed local stabilization in formation control,” in Proc. European Control Conf. (ECC), Strasbourg, France, 2014, pp. 2914–2919.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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.
    50. 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.
    51. 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. doi: 10.3182/20140824-6-ZA-1003.01938.
    52. 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.
    53. 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.
    54. R. Blind, “Optimization of the Communication System for Networked Control Systems,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2014.
    55. M. A. Müller, “Distributed and economic model predictive control: beyond setpoint stabilization,” Institute for Systems Theory and Automatic Control, University of Stuttgart, 2014.
  9. 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, Art. no. 4, 2013.
    2. R. Blind and F. Allgöwer, “On the Optimization of the Transport Layer for Networked Control Systems,” at-Automatisierungstechnik, vol. 61, no. 7, Art. no. 7, 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, Art. no. 1, 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, Art. no. 6, 2013.
    5. C. Feller, T. A. Johansen, and S. Olaru, “An improved algorithm for combinatorial multi-parametric quadratic programming,” Automatica, vol. 49, no. 5, Art. no. 5, 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, Art. no. 5, 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, Art. no. 10, 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, Art. no. 8, 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, Art. no. 4, 2013.
    12. G. Seyboth, D. V. Dimarogonas, and K. H. Johansson, “Event-based Broadcasting for Multi-agent Average Consensus,” Automatica, vol. 49, no. 1, Art. no. 1, 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. C. Vehlow et al., “iVUN: Interactive Visualization of Uncertain biochemical reaction Networks,” BMC Bioinf., vol. 14, p. 2, 2013.
    15. 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, Art. no. 10, 2013.
    16. 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, Art. no. 2, 2013.
    17. 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, Art. no. 1, 2013.
    18. D. Zelazo, S. Schuler, and F. Allgöwer, “Performance and design of cycles in consensus networks,” Syst. Contr. Lett., vol. 62, no. 1, Art. no. 1, 2013, doi: 10.1016/j.sysconle.2012.10.014.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    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. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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. doi: 10.1109/CDC.2013.6760844.
    49. M. Bürger, “Duality and Approximation Methods for Cooperative Optimization and Control,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2013.
    50. J. Hasenauer, “Modeling and parameter estimation for heterogeneous cell populations,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2013.
    51. 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.
    52. 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.
  10. 2012

    1. C. Böhm, M. Lazar, and F. Allgöwer, “Stability of periodically time-varying systems: Periodic Lyapunov functions,” Automatica, vol. 48, no. 10, Art. no. 10, 2012.
    2. 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, Art. no. 9, 2012.
    3. 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.
    4. 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, Art. no. 11, 2012.
    5. 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, Art. no. 2012, 2012.
    6. 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, Art. no. 8, 2012.
    7. 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, Art. no. 1, 2012.
    8. M. A. Müller and A. D. Dominguez-Garcia, “Fault coverage modeling in nonlinear dynamical systems,” Automatica, vol. 48, no. 7, Art. no. 7, 2012.
    9. 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, Art. no. 12, 2012.
    10. M. A. Müller and F. Allgöwer, “Improving performance in model predictive control: Switching cost functionals under average dwell-time,” Automatica, vol. 48, no. 2, Art. no. 2, 2012.
    11. M. A. Müller and D. Liberzon, “Input/output-to-state stability and state-norm estimators for switched nonlinear systems,” Automatica, vol. 48, no. 9, Art. no. 9, 2012.
    12. 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, Art. no. 9, 2012.
    13. N. Radde, “Analyzing fixed points of intracellular regulation networks with complex feedback topology,” BMC Sys. Biol., vol. 6, no. 57, Art. no. 57, 2012.
    14. M. Reble and F. Allgöwer, “Unconstrained Model Predictive Control and Suboptimality Estimates for Nonlinear Continuous-Time Systems,” Automatica, vol. 48, no. 8, Art. no. 8, 2012.
    15. 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, Art. no. 18, 2012.
    16. S. Yu, C. Böhm, H. Chen, and F. Allgöwer, “Model predictive control of constrained LPV systems,” Int. J. Control, vol. 85, no. 6, Art. no. 6, 2012.
    17. D. Zelazo, R. Dai, and M. Mesbahi, “An energy management system for off-grid power systems,” Energy Systems, vol. 3, no. 2, Art. no. 2, 2012.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. S. Waldherr, J. Hasenauer, and F. Allgöwer, “Set based uncertainty analysis and parameter estimation of biological networks with the BioSDP toolbox,” Ulm, Germany, 2012.
    45. 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. doi: 10.1109/CDC.2012.6426372.
    46. 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, pp. 473–480.
    47. 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. doi: 10.1109/CDC.2012.6426450.
    48. D. Zelazo and F. Allgöwer, “Growing Optimally Rigid Formations,” in Proc. American Control Conf. (ACC), Montreal, Canada, 2012, pp. 3901–3906.
    49. D. Zelazo and F. Allgöwer, “Eulerian Consensus Networks,” in Proc. 51st IEEE Conf. Decision and Control (CDC), Maui, HI, USA, 2012, pp. 4715–4720.
  11. 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, Art. no. 15, 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, Art. no. 4, 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, Art. no. 2, 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, Art. no. 10, 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, Art. no. 1, 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, Art. no. 8, 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, Art. no. 1, 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, Art. no. 12, 2011.
    10. N. Radde, “The role of feedback mechanisms in biological network models - A tutorial,” Asian J. Control, vol. 13, no. 5, Art. no. 5, 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, Art. no. 2, 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, Art. no. 1, 2011.
    13. K. Schmidt and C. Breindl, “Maximally Permissive Hierarchical Control of Decentralized Discrete Event Systems,” IEEE Trans. Autom. Control, vol. 56, no. 4, Art. no. 4, 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, Art. no. 12, 2011.
    15. R. Steuer, S. Waldherr, V. Sourjik, and M. Kollmann, “Robust Signal Processing in Living Cells,” PLoS Comp. Biol., vol. 7, no. 11, Art. no. 11, 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, Art. no. 5, 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, Art. no. 5, 2011.
    19. D. Zelazo and M. Mesbahi, “Graph-Theoretic Analysis and Synthesis of Relative Sensing Networks,” IEEE Trans. Autom. Control, vol. 56, no. 5, Art. no. 5, 2011, doi: 10.1109/TAC.2010.2085312.
    20. D. Zelazo and M. Mesbahi, “Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis,” IEEE Trans. Autom. Control, vol. 56, no. 3, Art. no. 3, 2011, doi: 10.1109/TAC.2010.2056730.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. H. B. Dürr, S. Zeng, and C. Ebenbauer, “Ein nichtlineares System zum Lösen von Sattelpunktproblemen und Linearen Programmen,” 2011.
    34. 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.
    35. 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.
    36. 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.
    37. J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgöwer, “Parameter estimation and uncertainty analysis for models of heterogeneous cell populations,” Heidelberg/Mannheim, Germany, 2011.
    38. 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,” Heidelberg/Mannheim, Germany, 2011.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. M. Löhning, J. Hasenauer, M. Khammash, and F. Allgöwer, “Optimierung mittels reduzierter Modelle mit garantierter Güte,” 2011.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. 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.
    56. 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.
    57. 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.
    58. 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.
    59. 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.
    60. 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.
    61. 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.
    62. 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.
    63. C. Böhm, “Predictive Control Using Semi-definite Programming - Efficient Approaches for Periodic Systems and Lur’e Systems,” University of Stuttgart, Stuttgart, Germany, 2011.
    64. S. Yu, “Robust Model Predictive Control of Constrained Systems,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2011.
  12. 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, Art. no. 6, 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, Art. no. 11, 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, Art. no. 9, 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, Art. no. 2, 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, Art. no. 1, 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, Art. no. 1, 2010.
    8. U. Münz, A. Papachristodoulou, and F. Allgöwer, “Delay Robustness in Consensus Problems,” Automatica, vol. 46, no. 8, Art. no. 8, 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, Art. no. 4, 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, Art. no. 6, 2010.
    11. N. Radde, “Fixed point characterization of biological networks with complex graph topology,” Bioinformatics, vol. 26, no. 22, Art. no. 22, 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, Art. no. 4, 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, Art. no. 10, 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. Böhm and F. Allgöwer, “Efficient offline model predictive control of constrained nonlinear periodic systems,” Antalya, Turkey, 2010.
    20. 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.
    21. C. Böhm, M. Lazar, and F. Allgöwer, “Stability analysis of periodically time-varying systems using periodic Lyapunov functions,” Antalya, Turkey, 2010.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. J. Hasenauer et al., “Single-cells vs. cell populations - From a binary decision to a continuous response,” Freiburg, Germany, 2010.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. M. A. Müller and A. D. Domínguez-García, “On Input-to-State Stability Notions for Reachability Analysis of Power Systems,” 2010.
    35. 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.
    36. 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.
    37. M. Reble and F. Allgöwer, “Stabilizing design parameters for model predictive control of constrained nonlinear time-delay systems,” Prague, Czech Republic, 2010. doi: 10.3182/20100607-3-CZ-4010.00064.
    38. 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.
    39. D. Schlipf, S. Schuler, P. Grau, F. Allgöwer, and M. Kühn, “Look-Ahead Cyclic Pitch Control Using LIDAR,” 2010.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. 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.
    50. 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. doi: 10.1109/ACC.2010.5530963.
    51. U. Münz, “Delay Robustness in Cooperative Control,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2010.
    52. 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.
    53. 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.
  13. 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, Art. no. 1, 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, Art. no. 52, 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, Art. no. 9, 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, Art. no. 9, 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, Art. no. 4, 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, Art. no. 5, 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, Art. no. 5, 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, Art. no. 1, 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, Art. no. 7, 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. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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, Zürich, Switzerland, 16-20 July 2007, R. Jeltsch and G. Wanner, Eds. Zürich, Switzerland: European Mathematical Society Publishing House, 2009, pp. 23–42.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. C. Böhm, S. Yu, and F. Allgöwer, “Predictive control for constrained discrete-time periodic systems using a time-varying terminal region,” Miedzyzdroje, Poland, 2009.
    34. 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.
    35. 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.
    36. 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.
    37. C. Ebenbauer and A. Arsie, “Refining Lasalle’s invariance principle,” in Proc. American Control Conf. (ACC), St. Louis, Missouri, USA, 2009, pp. 108–112.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
    48. 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.
    49. N. Radde, N. S. Bar, and A. Tresch, “A comparison of likelihoods for dynamic stochastic models of biological networks,” 2009.
    50. M. Reble and F. Allgöwer, “Modellprädiktive Regelung für nichtlineare Totzeitsysteme,” 2009.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. 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.
    56. 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.
    57. 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.
    58. 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.
    59. 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.
    60. 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.
    61. 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.
    62. 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.
  14. 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, Art. no. 1, 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, Art. no. Special Issue, 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, Art. no. 1, 2008.
    4. C. Ebenbauer and F. Allgöwer, “A Dissipation Inequality for the Minimum Phase Property,” IEEE Trans. Autom. Control, vol. 53, no. 3, Art. no. 3, 2008.
    5. 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, Art. no. 1, 2008.
    6. 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, Art. no. 1, 2008.
    7. 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.
    8. 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, Art. no. 2, 2008.
    9. 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, Art. no. 3, 2008.
    10. N. Radde and L. Kaderali, “Inference of an oscillating model for the yeast cell cycle,” Discrete Appl. Math., vol. 157, no. 10, Art. no. 10, 2008, doi: 10.1016/j.dam.2008.06.036.
    11. 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.
    12. S. Waldherr and M. Zeitz, “Conditions for the existence of a flat input,” Int. J. Control, vol. 81, no. 3, Art. no. 3, 2008.
    13. K. Yao, F. G., and F. Allgöwer, “Barrel temperature control during operation transition in injection molding,” Control Engineering Practice, vol. 16, no. 11, Art. no. 11, 2008, doi: 10.1016/j.conengprac.2008.02.003.
    14. M. Ahdesmäki et al., Eds., Proc. 5th Int. Workshop on Comput. Syst. Biol. (WCSB08). 2008.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. J. Hasenauer, S. Waldherr, and F. Allgöwer, “Global sensitivity analysis of biochemical reaction networks using semidefinite programming,” Gothenburg, Sweden, 2008.
    23. 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.
    24. 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.
    25. 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.
    26. U. Münz, P. Schumm, and F. Allgöwer, “Educational Games in Control,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 12625–12630.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. S. Waldherr, T. Eißing, and F. Allgöwer, “Analysis of Feedback Mechanisms in Cell-biological Systems,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 15861–15866.
    35. S. Waldherr, R. Findeisen, and F. Allgöwer, “Global Sensitivity Analysis of Biochemical Reaction Networks via Semidefinite Programming,” in Proc. 17th IFAC World Congress, Seoul, Korea, 2008, pp. 9701–9706.
    36. 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,” 2008.
    37. S. Waldherr, J. Hasenauer, and F. Allgöwer, “Global sensitivity analysis of uncertain biochemical reaction networks,” 2008.
    38. 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.
    39. S. Yu, H. Chen, C. Böhm, and F. Allgöwer, “Moving horizon $H_ınfty$ control based on T-S models,” Pavia, Italy, 2008.
  15. 2007

    1. C. Ebenbauer and F. Allgöwer, “Stability Analysis of Constrained Control Systems: An Alternative Approach,” Syst. Contr. Lett., vol. 56, no. 2, Art. no. 2, 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, Art. no. 4, 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, Art. no. 9, 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, Art. no. 3, 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, Art. no. 3, 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, Art. no. S07, 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, Art. no. 4, 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, Art. no. 6, 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, Art. no. 17, 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, Art. no. 7, 2007.
    12. T. Schweickhardt and F. Allgöwer, “Linear control of nonlinear systems based on nonlinearity measures,” J. Proc. Contr., vol. 17, no. 3, Art. no. 3, 2007.
    13. 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.
    14. 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.
    15. F. Allgöwer, L. del Re, M. Diehl, and R. Scattolini, Eds., Predictive Control of Combustion Engines. Trauner Verlag, 2007.
    16. R. Findeisen, L. B. Biegler, and F. Allgöwer, Eds., Assessment and Future Directions of Nonlinear Model Predictive Control, no. 358. Springer Berlin / Heidelberg, 2007.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. L. Kaderali and N. Radde, “Inferring gene regulatory networks from expression data,” in Studies in Computational Intelligence, vol. 1, Springer, 2007.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. J.-S. Kim and F. Allgöwer, “Nonlinear Observer-based Synchronization of Neuron Models,” Potsdam, Germany, 2007.
    30. 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.
    31. J. Maess and F. Allgöwer, “Closed-Loop Simulation of Kelvin Probe Force Microscopy based on Reduced Finite Element Cantilever Modeling,” Potsdam, Germany, 2007.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. U. Münz and F. Allgöwer, “$L_2$-Gain Based Controller Design for Linear Systems with Distributed Delays and Rational Delay Kernels,” Nantes, France, 2007.
    37. 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.
    38. 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.
    39. 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.
    40. T. Raff and F. Allgöwer, “An Impulsive Observer that Estimates the Exact State of a Linear Continuous-Time System in Predetermined Finite Time,” Athens, Greece, 2007.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. S. Waldherr, T. Eißing, and F. Allgöwer, “Analysing biological feedback with tools from control theory,” 2007.
    46. 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.
    47. 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.
    48. 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.
    49. T. Eißing, “A systems science view on cell death signalling,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2007.
    50. 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.
  16. 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, Art. no. 4, 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, Art. no. 7, 2006.
    5. N. Radde, J. Gebert, and C. V. Forst, “Systematic component selection for gene-network refinement,” Bioinformatics, vol. 22, no. 21, Art. no. 21, 2006.
    6. J. M. Rieber and F. Allgöwer, “From $H_ınfty$ control to multiobjective control: an overview,” at-Automatisierungstechnik, vol. 54, no. 9, Art. no. 9, 2006.
    7. 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, Art. no. 13, 2006.
    8. R. Findeisen, Nonlinear Model Predictive Control: A Sampled-Data Feedback Perspective. Düsseldorf: Fortschr.-Ber. VDI Reihe 8 Nr. 1087, VDI Verlag, 2006.
    9. 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.
    10. 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.
    11. 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.
    12. S. Borchers, S. Maldonado, R. Findeisen, and F. Allgöwer, “Modeling the bone remodeling cycle due to mechanical force,” in Proc. Int. Mediterranean Modelling Conf.\, 2nd European Modeling and Simulation Symp. (EMSS), Barcelona, Spain, 2006, pp. 385–394.
    13. E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead, “System and control theory allows to further understanding of biological signal transduction,” 2006.
    14. M. Chaves, T. Eißing, and F. Allgöwer, “Identifying mechanisms for bistability in an apoptosis network,” Lyon, France, 2006.
    15. 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.
    16. M. Chaves, “Stability of rate-controlled zero-deficiency networks,” in Proc. 45th IEEE Conf. Decision and Control (CDC), 2006, pp. 5766–5771.
    17. C. Ebenbauer and F. Allgöwer, “Polynomial Control Systems: Analysis and Design via Dissipation Inequalities,” 2006.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. T. Eißing, S. Waldherr, F. Allgöwer, and E. Bullinger, “Modelling and Analysis of Death and Survival Signalling: Achievements and Trends,” Toulouse, France, 2006.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. T. Raff, F. Lachner, and F. Allgöwer, “A Finite Time Unknown Input Observer for Linear Systems,” Ancona, Italy, 2006.
    34. 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.
    35. J. M. Rieber, C. W. Scherer, and F. Allgöwer, “On complexity issues in multiobjective controller design using convex optimization,” Toulouse, France, 2006.
    36. J. M. Rieber and F. Allgöwer, “Gain-scheduling in the $\ell_1$ framework: a flight control example,” Toulouse, France, 2006.
    37. T. Schweickhardt, P. Schumm, U. Münz, and F. Allgöwer, “Integration of E-Learning Modules in Automatic Control Education,” Madrid, Spain, 2006.
    38. 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.
    39. 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.
    40. H. Shim, J. Lee, J.-S. Kim, and J. Back, “Output Regulation Problem and Solution for LTV Minimum Phase Systems with Time-varying Exosystem,” 2006.
    41. 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.
    42. S. Waldherr and F. Allgöwer, “Hopf bifurcations and feedback gain in signaling pathways,” Heidelberg, Germany, 2006.
    43. S. Waldherr, T. Eißing, M. Chaves, and F. Allgöwer, “Preservation of bistability in the reduction of an apoptosis model,” Manchester, UK, 2006.
    44. 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.
    45. T. Schweickhardt, “Nonlinearity Assessment and Linear Control of Nonlinear Systems,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2006.
  17. 2005

    1. F. Allgöwer, “Editorial: Nonlinear Model Predictive Control,” IEE Control Theory Appl., vol. 152, no. 3, Art. no. 3, 2005.
    2. E. Bullinger and F. Allgöwer, “Adaptive $łambda$-tracking for nonlinear higher relative degree systems,” Automatica, vol. 41, no. 7, Art. no. 7, 2005.
    3. 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, Art. no. 3, 2005.
    4. C. Ebenbauer, T. Raff, and F. Allgöwer, “Passivity-based Feedback Design for Polynomial Control Systems,” at-Automatisierungstechnik, vol. 8, pp. 356–366, 2005.
    5. 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, Art. no. 4, 2005.
    6. T. Ergenc, S. Pickl, N. Radde, and G. Weber, “Generalized semi-infinite optimization and anticipatory systems,” Int. J. Comput. Anticipatory Syst., vol. 15, pp. 3–30, 2005.
    7. Z. K. Nagy, R. Roman, S. P. Agachi, and F. Allgöwer, “First principles modeling and nonlinear optimization based estimation and control of a fluid catalytic cracking unit,” Studia Universitatis Babes-Bolyai. Ser. Chemia, no. 2, Art. no. 2, 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, Art. no. 4–5, 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, Art. no. 1, 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. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. C. Ebenbauer, T. Raff, and F. Allgöwer, “A Simple Separation Result for Control Affine Systems,” 2005.
    20. 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.
    21. 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.
    22. C. Hüttner, J. M. Rieber, F. Allgöwer, and J. Hugel, “Compensation of time-varying harmonic disturbances on nonlinear bearingless slice motors,” Prague, Czech Republic, 2005.
    23. U. Münz and P. J. Zufiria, “Parametric Fault Diagnosis in Stochastic Dynamical Systems,” 2005.
    24. Z. K. Nagy, B. Mahn, F. Ruediger, and F. Allgöwer, “Nonlinear model predictive control of batch processes: an industrial case study,” Prague, Czech Republic, 2005.
    25. 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.
    26. 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.
    27. 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.
    28. T. Raff, C. Ebenbauer, and F. Allgöwer, “Nonlinear Model Predictive Control: A Passivity-based Approach,” 2005.
    29. 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.
    30. 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.
    31. J. M. Rieber, G. Schitter, A. Stemmer, and F. Allgöwer, “Experimental application of $\ell_1$-optimal control in atomic force microscopy,” Prague, Czech Republic, 2005.
    32. 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.
    33. 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.
    34. T. Sauter et al., “Mathematical modeling of TNF induced apoptotic and anti-apoptotic crosstalk in mammalian cells,” Boston, MA, 2005.
    35. 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.,” Potsdam, Germany, 2005.
    36. 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.
    37. 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.
    38. 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.
  18. 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, Art. no. 3, 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, Art. no. 1, 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, Art. no. 35, 2004.
    4. J. Gebert, H. Oktem, S. Pickl, N. Radde, G. Weber, and F. Yılmaz, “Inference of gene expression patterns by using a hybrid system formulation -- an algorithmic approach to local state transition matrices,” Anticipative and Predictive Models in Systems Science I, Lasker, GE, and Dubois, DM (eds.), IIAS (International Institute for Advanced Studies) in Windsor, Ontario, pp. 63–66, 2004.
    5. A. Kremling et al., “A benchmark for methods in reverse engineering and model discrimination: problem formulation and solutions,” Genome Research, vol. 14, no. 9, Art. no. 9, 2004.
    6. A. Rehm and F. Allgöwer, “$H_ınfty$ Regelung von zeitdiskreten Deskriptorsystemen,” at-Automatisierungstechnik, vol. 52, no. 9, Art. no. 9, 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, Art. no. 9, 2004.
    8. J. M. Rieber, H. Wehlan, and F. Allgöwer, “The ROBORACE contest,” IEEE Control Systems Magazine, vol. 24, no. 5, Art. no. 5, 2004.
    9. T. Sauter and E. Bullinger, “Detailed mathematical modeling of metabolic and regulatory networks,” BIOforum Europe, vol. 2004, no. 2, Art. no. 2, 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, Art. no. 2, 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, Art. no. 2, 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,” 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,” 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,” 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. 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. 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.
    36. A. Rehm and F. Allgöwer, “Causal  $ H_ınfty$ Control of Discrete-time Descriptor Systems: An LMI Approach in two Steps,” Leuven, Belgium, 2004.
    37. 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.
    38. 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.
    39. 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 für Instandhaltung, Diagnose und Anlagenüberwachung, Aachen,Germany, 2004, pp. 399–410.
    40. 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.
    41. 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.
    42. R. Findeisen, “Nonlinear model predictive control : a sampled data feedback perspective,” Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany, 2004.
    43. 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.
  19. 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, Art. no. 1, 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, Art. no. 12, 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, Art. no. 2–3, 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, Art. no. 3–4, 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, Art. no. 3, 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, Art. no. 7, 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, Art. no. 3–4, 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,” 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. 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.
    15. P. H. Menold, R. Findeisen, and F. Allgöwer, “Finite time convergent observers for linear time-varying systems,” Rhodes, Greece, 2003.
    16. 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.
    17. P. H. Menold and F. Allgöwer, “Finite time convergent observer,” San Francisco, CA, USA, 2003.
    18. A. Rehm and F. Allgöwer, “$H_ınfty$ control of descriptor systems: An application from binary distillation control,” Cambridge, UK, 2003.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. T. Schweickhardt, F. Allgöwer, and F. J. Doyle III, “The optimal control law nonlinearity measure: Improving control-relevant nonlinearity assessment,” San Francisco, CA, USA, 2003.
  20. 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, Art. no. 12, 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, Art. no. 12, 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, Art. no. 4, 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,” Barcelona, Spain, 2002.
    6. C. Burger, E. Bullinger, S. Papakosta, and T. Wagner, “Context awareness for application sharing in teaching environment,” 2002.
    7. C. Burger, S. Papakosta, E. Bullinger, and T. Wagner, “Investigation of application sharing systems for teaching purposes in engineering disciplines,” 2002.
    8. 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.
    9. 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.
    10. R. Findeisen, L. Imsland, F. Allgöwer, and B. A. Foss, “Output feedback nonlinear predictive control - A separation principle approach.,” Barcelona, Spain, 2002.
    11. H. W. Knobloch, C. Ebenbauer, and F. Allgöwer, “A framework for disturbance attenuation with discontinuous control,” Barcelona, Spain, 2002.
    12. L. Magni, G. de Nicolao, R. Scattolini, and F. Allgöwer, “Robust receding horizon control for nonlinear discrete-time systems,” Barcelona, Spain, 2002.
    13. 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.
    14. 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.
    15. A. Rehm and F. Allgöwer, “An LMI Approach towards Stabilization of Discrete-time Descriptor Systems,” Barcelona, Spain, 2002.
    16. 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.
    17. B. Schoeberl, T. Eißing, M. Fotin, E. D. Gilles, and P. Scheurich, “A mathematical model of TNF receptor interaction,” Stuttgart, Germany, 2002.
  21. 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, Art. no. 8, 2001.
    2. F. Allgöwer and R. Findeisen, “Nonlinear Model Predictive Control of Chemical Processes,” in Workshop on Geometrical Modeling and Control of Physical Systems, B. Maschke and A. Van der Schaft, Eds. Grenoble, France: Ecole d’Etè d’Automatique de Grenoble, 2001, pp. 3.1-3.75.
    3. 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.
    4. R. Findeisen and F. Allgöwer, “An introduction to nonlinear model predictive control,” in Summerschool on ``The Impact of Optimization in Control’’, C. W. Scherer and J. M. Schumacher, Eds. Delft, The Netherlands: Dutch Institute of Systems and Control (DISC), 2001.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Z. Nagy, S. P. Agachi, F. Allgöwer, and R. Findeisen, “Nonlinear model predictive control of a high purity distillation column,” Prague, Czech Republic, 2001.
    12. 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.
    13. R. K. Pearson, P. H. Menold, and F. Allgöwer, “Structured Outliers and Data Cleaning Filters,” Baltimore, MD, USA, 2001.
  22. 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, Art. no. 1, 2000.
    2. 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, Art. no. 1–2, 2000.
    3. A. Rehm and F. Allgöwer, “Self-Scheduled $H_ınfty$ Output Feedback Control of Descriptor Systems,” Comp. & Chem. Eng., vol. 24, no. 2–7, Art. no. 2–7, 2000.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. R. K. Pearson, P. H. Menold, and F. J. Kraus, “Set-theoretic input sequence design for orthonormal model identification,” Santa Barbara, California, USA, 2000.
    13. E. Bullinger, “Adaptive $łambda$-tracking for Systems with Higher Relative Degree,” Swiss Federal Institute of Technology (ETH), 2000.
  23. 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, Art. no. 3, 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,” 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.
  24. 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, Art. no. 5–6, 1998.
    2. H. Chen and F. Allgöwer, “A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability,” Automatica, vol. 34, no. 10, Art. no. 10, 1998.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
  25. 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. 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.
    3. E. Bullinger and F. Allgöwer, “Ein adaptiver high-gain Beobachter für nichtlineare Systeme,” 1997.
    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,” 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,” Bruessels, Belgium, 1997.
  26. 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.
  27. 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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