This picture showsFrank Allgöwer

Prof. Dr.-Ing.

Frank Allgöwer

Head of institute
Institute for Systems Theory and Automatic Control

Contact

+49 711 685-67733
+49 711 685-67735

Pfaffenwaldring 9
70569 Stuttgart
Germany
Room: 2.246

Office Hours

The office hours of Prof. Allgöwer will be conducted via WebEx at
https://unistuttgart.webex.com/meet/frank.allgoewer
and take place Monday 1 - 2 pm and Friday 12 noon - 1 pm.

  1. (Journal-) Articles

    1. J. Bongard, J. Berberich, J. Köhler, and F. Allgöwer, “Robust stability analysis of a simple data-driven model predictive control approach,” IEEE Trans. Automat. Control, 2021.
    2. M. Alsalti, J. Berberich, V. G. Lopez, F. Allgöwer, and M. A. Müller, “Data-Based System Analysis and Control of Flat Nonlinear Systems,” IEEE Control Systems Lett., 2021.
    3. T. Martin and F. Allgöwer, “Dissipativity Verification With Guarantees for Polynomial Systems From Noisy Input-State Data,” IEEE Control Systems Letters, vol. 5, no. 4, Art. no. 4, 2021, doi: 10.1109/LCSYS.2020.3037842.
    4. 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, doi: 10.1016/j.arcontrol.2020.11.002.
    5. J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining prior knowledge and data for robust controller design,” IEEE Trans. Automat. Control, 2020.
    6. 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.
    7. A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input-output data,” IEEE Control Systems Letters, vol. 3, pp. 709--714, 2019.
    8. 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.
    9. 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.
    10. S. Wildhagen, M. A. Müller, and F. Allgöwer, “Predictive Control over a Dynamical Token Bucket Network,” IEEE Control Systems Letters, vol. 3, no. 4, Art. no. 4, 2019.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
  2. Conference papers

    1. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “On the design of terminal ingredients for data-driven MPC,” Bratislava, Slovakia, 2021.
    2. J. Berberich, S. Wildhagen, M. Hertneck, and F. Allgöwer, “Data-driven analysis and control of continuous-time systems under aperiodic sampling,” Padova, Italy, 2021.
    3. N. Wieler, J. Berberich, A. Koch, and F. Allgöwer, “Data-driven controller design via finite-horizon dissipativity,” Zürich, Switzerland, 2021.
    4. 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, doi: 10.1109/CDC42340.2020.9303819.
    5. T. Martin and F. Allgöwer, “Data-driven surrogate models for LTI systems via saddle-point dynamics,” 2020.
    6. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Robust Economic Model Predictive Control without Terminal Conditions,” 2020, doi: 10.1016/j.ifacol.2020.12.465.
    7. T. Martin and F. Allgöwer, “Iterative data-driven inference of nonlinearity measures via successive graph approximation,” in 2020 59th IEEE Conference on Decision and Control (CDC), 2020, pp. 4760–4765, doi: 10.1109/CDC42340.2020.9304285.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. J. Berberich, M. Sznaier, and F. Allgöwer, “Signal estimation and system identification with nonlinear dynamic sensors,” in 3rd IEEE Conference on Control Technology and Applications, 2019, pp. 505–510.
    13. T. Martin and F. Allgöwer, “Nonlinearity measures for data-driven system analysis and control,” in 2019 IEEE 58th Conference on Decision and Control (CDC), 2019, pp. 3605–3610, doi: 10.1109/CDC40024.2019.9029804.
    14. 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.
    15. P. Wenzelburger and F. Allgöwer, “A Petri Net Modeling Framework for the Control of Flexible Manufacturing Systems,” in 9th IFAC Conference on Manufacturing Modeling, Management, and Control, Berlin, Germany, 2019, pp. 492–498.
    16. 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.
    17. T. Martin and F. Allgöwer, “Nonlinearity Measures for Data-Driven System Analysis and Control,” in Proc. 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019, pp. 3605–3610.
    18. T. Martin, P. N. Köhler, and F. Allgöwer, “Dissipativity and Economic Model Predictive Control for Optimal Set Operation,” in Proc.\ American Control Conf.\ (ACC), Philadelphia, USA, 2019, pp. 1020–1026.
    19. W. Halter, S. Michalowsky, and F. Allgöwer, “Extremum seeking for optimal enzyme production under cellular fitness constraints,” Neapel, Italien, 2019.
    20. 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.
    21. R. Soloperto, J. Köhler, M. A. Müller, and F. Allgöwer, “Dual Adaptive MPC for output tracking of linear systems,” Nice, France, 2019.
    22. P. Wenzelburger and F. Allgöwer, “A Novel Optimal Online Scheduling Scheme for Flexible Manufacturing Systems,” in 13th IFAC Workshop on Intelligent Manufacturing Systems, Oshawa, Canada, 2019, pp. 1–6.
    23. 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.
    24. 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.
    25. 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.
    26. R. Soloperto, P. N. Köhler, M. A. Müller, and F. Allgöwer, “Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty,” Madison, Wisconsin, 2018.
  3. Other

    1. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-Driven Model Predictive Control with Stability and Robustness Guarantees.” 2019.
    2. S. Linsenmayer and F. Allgöwer, “Control over Networks Using a Slotted Transmission Classication Model.” 2019.
    3. S. Linsenmayer and F. Allgöwer, “Networked Control Systems with advanced interfaces between control and communication.” 2018.
    4. W. Halter and F. Allgöwer, “Regelungstechnik in der Synthetischen Biologie: Konzeptionelle und experimentelle Realisierung von PID Reglern im Inneren von Zellen.” 2018.
    5. J. Köhler and F. Allgöwer, “Robust reference tracking with Model Predictive Control.” 2018.
    6. J. Berberich and F. Allgöwer, “A convex relaxation for learning linear dynamical systems with nonlinear sensors.” 2018.
    7. D. Imig, N. Pollak, and F. Allgöwer, “Cell population dynamics during apoptotic treatment.” 2017.
  4. Preprints

    1. C. Klöppelt, L. Schwenkel, F. Allgöwer, and M. A. Müller, “Transient Performance of Tube-based Robust Economic Model Predictive Control,” arXiv:2102.09404, 2021.
    2. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Model predictive control for linear uncertain systems using integral quadratic constraints,” arXiv:2104.05444, 2021.

Frank Allgöwer is director of the Institute for Systems Theory and Automatic Control and professor in Mechanical Engineering at the University of Stuttgart in Germany.

Frank's main interests in research and teaching are in the area of systems and control with a current emphasis on the development of new methods for data-based control, optimiza-tion- based control, networks of systems, and systems biology. Frank received several recogni- tions for his work including the IFAC Outstanding Service Award, the IEEE CSS Distinguished Member Award, the State Teaching Award of the German state of Baden-Württemberg, and the Leibniz Prize of the Deutsche Forschungsgemeinschaft. Frank has been the President of the International Federation of Automatic Control (IFAC) for the years 2017-2020. He was Editor for the journal Automatica from 2001 to 2015 and is editor for the Springer Lecture Notes in Control and Information Science book series and has published over 900 scientific articles. From 2012 until 2020 Frank served a Vice-President of Germany's most important research funding agency the German Research Foundation (DFG).

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