This image shows Frank Allgöwer

Frank Allgöwer

Prof. Dr.-Ing.

Head of the institute
Institute for Systems Theory and Automatic Control

Contact

+49 711 685 67733

Pfaffenwaldring 9
70569 Stuttgart
Germany
Room: 2.246

Office Hours

Monday: 1:00 pm - 2:00 pm
Room 2.245

Further office hours by appointment.

Secretaries:
Yaryna Svyryda: +49 711 685-67736  -  yaryna.svyryda@ist.uni-stuttgart.de
Jasmin Winkler: +49 711 685-67731  -  jasmin.winkler@ist.uni-stuttgart.de

  1. (Journal-) Articles

    1. R. Strässer, J. Berberich, M. Schaller, K. Worthmann, and F. Allgöwer, “Koopman-based control of nonlinear systems with closed-loop guarantees,” at-Automatisierungstechnik, Preprint: arxiv:2411.10359, 2025.
    2. L. Schwenkel, J. Köhler, M. A. Müller, C. W. Scherer, and F. Allgöwer, “Multi-objective robust controller synthesis with integral quadratic constraints in discrete-time,” arXiv:2503.22429, 2025.
    3. R. Strässer, M. Schaller, K. Worthmann, J. Berberich, and F. Allgöwer, “Koopman-based feedback design with stability guarantees,” IEEE Transactions on Automatic Control, vol. 70, Art. no. 1, 2025, doi: 10.1109/TAC.2024.3425770.
    4. R. Strässer, M. Schaller, J. Berberich, K. Worthmann, and F. Allgöwer, “Kernel-based error bounds of bilinear Koopman surrogate models for nonlinear data-driven control,” submitted, Preprint: arxiv:2503.13407, 2025.
    5. M. Köhler, M. A. Müller, and F. Allgöwer, “Distributed Model Predictive Control for Dynamic Cooperation of Multi-Agent Systems,” preprint on arxiv, 2025, doi: 10.48550/arXiv.2504.00225.
    6. R. Strässer, S. Schlor, and F. Allgöwer, “Decrypting Nonlinearity: Koopman Interpretation and Analysis of Cryptosystems,” Automatica, vol. 173, p. 112022, 2025, doi: 10.1016/j.automatica.2024.112022.
    7. J. Mair, L. Schwenkel, M. A. Müller, and F. Allgöwer, “The Cesàro Value Iteration,” arXiv:2504.04889, 2025, doi: 10.48550/arXiv.2504.04889.
    8. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints,” arXiv:2504.00196, 2025.
    9. A. Bozza, T. Martin, G. Cavone, R. Carli, M. Dotoli, and F. Allgöwer, “Online Data-Driven Control of Nonlinear Systems Using Semidefinite Programming,” IEEE Control Systems Lett., vol. 8, pp. 3189–3194, 2024, doi: 10.1109/LCSYS.2024.3521645.
    10. L. Schwenkel, A. Hadorn, M. A. Müller, and F. Allgöwer, “Linearly discounted economic MPC without terminal conditions for periodic optimal operation,” Automatica, vol. 159, p. 111393, 2024, doi: 10.1016/j.automatica.2023.111393.
    11. M. Köhler, M. A. Müller, and F. Allgöwer, “Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems,” IEEE Transactions on Automatic Control, pp. 1–8, 2024, doi: 10.1109/TAC.2024.3407633.
    12. M. Seidel, M. Hertneck, P. Yu, S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “A Window-based Periodic Event-triggered Consensus Scheme for Multi-agent Systems,” IEEE Transactions on Control of Network Systems, vol. 11, Art. no. 1, Mar. 2024, doi: 10.1109/tcns.2023.3285863.
    13. N. Chatzikiriakos, R. Strässer, F. Allgöwer, and A. Iannelli, “End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data,” submitted, Preprint: arXiv:2409.18010, 2024.
    14. D. Meister, D. J. Antunes, and F. Allgöwer, “How improving performance may imply losing consistency in event-triggered consensus,” submitted, 2024, [Online]. Available: https://arxiv.org/abs/2405.03245
    15. T. Martin and F. Allgöwer, “Data-Driven System Analysis of Nonlinear Systems Using Polynomial Approximation,” IEEE Transactions on Automatic Control, vol. 69, Art. no. 7, 2024, doi: 10.1109/TAC.2023.3321212.
    16. M. Hertneck and F. Allgöwer, “Robust dynamic self-triggered control for nonlinear systems using hybrid Lyapunov functions,” Nonlinear Analysis: Hybrid Systems, vol. 53, p. 101485, 2024, doi: 10.1016/j.nahs.2024.101485.
    17. D. Meister, F. Aurzada, M. A. Lifshits, and F. Allgöwer, “Time- versus event-triggered consensus of a single-integrator multi-agent system,” Nonlinear Analysis: Hybrid Systems, vol. 53, p. 101494, 2024, doi: 10.1016/j.nahs.2024.101494.
    18. M. Hertneck, S. Lang, J. Berberich, and F. Allgöwer, “Event-Triggered Control Based on Integral Quadratic Constraints,” IEEE Control Systems Letters, vol. 8, pp. 2039–2044, 2024, doi: 10.1109/LCSYS.2024.3427989.
    19. S. Schlor and F. Allgöwer, “Bootstrapping Guarantees: Stability and Performance Analysis for Dynamic Encrypted Control,” IEEE Control Systems Lett., vol. 8, pp. 2235–2240, 2024, doi: 10.1109/LCSYS.2024.3398197.
    20. J. Venkatasubramanian, J. Köhler, J. Berberich, and F. Allgöwer, “Sequential learning and control: Targeted exploration for robust performance,” IEEE Trans. Automat. Control, vol. 70, Art. no. 1, 2024, doi: 10.1109/TAC.2024.3430088.
    21. M. Köhler, M. A. Müller, and F. Allgöwer, “Distributed Economic MPC with Adaptive Terminal Weights,” IFAC-PapersOnLine, vol. 58, Art. no. 18, 2024, doi: 10.1016/j.ifacol.2024.09.016.
    22. M. Seidel, S. Lang, and F. Allgöwer, “On l2-performance of weakly-hard real-time control systems,” European Journal of Control, p. 101056, Jun. 2024, doi: 10.1016/j.ejcon.2024.101056.
    23. R. Strässer, M. Schaller, K. Worthmann, J. Berberich, and F. Allgöwer, “SafEDMD: A certified learning architecture tailored to data-driven control of nonlinear dynamical systems,” submitted, Preprint: arxiv:2402.03145, 2024.
    24. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Model predictive control for linear uncertain systems using integral quadratic constraints,” IEEE Trans. Automat. Control, vol. 68, Art. no. 1, 2023, doi: 10.1109/TAC.2022.3171410.
    25. T. Martin, T. B. Schön, and F. Allgöwer, “Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey,” Annual Reviews in Control, vol. 56, p. 100911, 2023, doi: 10.1016/j.arcontrol.2023.100911.
    26. 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, vol. 68, Art. no. 5, 2023, doi: 10.1109/TAC.2022.3163110.
    27. A. Alanwar, A. Koch, F. Allgöwer, and F. H. Johansson, “Data-Driven Reachability Analysis from Noisy Data,” IEEE Transactions on Automatic Control, pp. 1–16, 2023, doi: 10.1109/TAC.2023.3257167.
    28. V. Wagner, R. Strässer, F. Allgöwer, and N. E. Radde, “A provably convergent control closure scheme for the Method of Moments of the Chemical Master Equation,” Journal of Chemical Theory and Computation, vol. 19, Art. no. 24, Dec. 2023, doi: https://doi.org/10.1021/acs.jctc.3c00548.
    29. J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining prior knowledge and data for robust controller design,” IEEE Trans. Automat. Control, vol. 68, Art. no. 8, 2023, doi: 10.1109/TAC.2022.3209342.
    30. X. Wang, J. Sun, J. Berberich, G. Wang, F. Allgöwer, and J. Chen, “Data-driven Control of Dynamic Event-triggered Systems with Delays,” Int. J. Robust and Nonlinear Control, vol. 33, pp. 7071–7093, 2023, doi: 10.1002/rnc.6740.
    31. P. N. Köhler, M. A. Müller, and F. Allgöwer, “Approximate Dissipativity of Cost-Interconnected Systems in Distributed Economic MPC,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 4, 2023, doi: 10.1109/TAC.2022.3173028.
    32. R. Soloperto, J. Köhler, and F. Allgöwer, “A Nonlinear MPC Scheme for Output Tracking Without Terminal Ingredients,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 4, 2023, doi: 10.1109/TAC.2022.3173494.
    33. T. Martin and F. Allgöwer, “Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures,” IEEE Trans. Automat. Control, vol. 68, Art. no. 5, 2023, doi: 10.1109/TAC.2022.3226652.
    34. M. Köhler, L. Krügel, L. Grüne, M. A. Müller, and F. Allgöwer, “Transient Performance of MPC for Tracking,” IEEE Control Systems Letters, vol. 7, pp. 2545–2550, 2023, doi: 10.1109/LCSYS.2023.3287798.
    35. Y. Xie, J. Berberich, and F. Allgöwer, “Linear Data-Driven Economic MPC with Generalized Terminal Constraint,” IFAC World Congress, 2023.
    36. M. Köhler, M. A. Müller, and F. Allgöwer, “Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.1450.
    37. X. Wang, J. Berberich, J. Sun, G. Wang, F. Allgöwer, and J. Chen, “Model-based and data-driven control of event-and self-triggered discrete-time systems,” IEEE Trans. Cybernetics, vol. 53, Art. no. 9, 2023, doi: 10.1109/TCYB.2023.3272216.
    38. M. Alsalti, V. G. Lopez, J. Berberich, F. Allgöwer, and M. A. Müller, “Data-based control of feedback linearizable systems,” IEEE Trans. Automat. Control, vol. 68, Art. no. 11, 2023, doi: 10.1109/TAC.2023.3249289.
    39. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” IEEE Trans. Automat. Control (early access), 2023, doi: 10.1109/TAC.2023.3321212.
    40. R. Soloperto, M. A. Müller, and F. Allgöwer, “Guaranteed Closed-Loop Learning in Model Predictive Control,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 2, 2023, doi: 10.1109/TAC.2022.3172453.
    41. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Linear tracking MPC for nonlinear systems part II: the data-driven case,” IEEE Trans. Automat. Control, vol. 67, Art. no. 9, 2022, doi: 10.1109/TAC.2022.3166851.
    42. P. Pauli, J. Berberich, and F. Allgöwer, “Robustness analysis and training of recurrent neural networks using dissipativity theory,” at - Automatisierungstechnik, vol. 70, Art. no. 8, 2022, doi: 10.1515/auto-2022-0032.
    43. C. Klöppelt, J. Berberich, F. Allgöwer, and M. A. Müller, “A novel constraint-tightening approach for robust data-driven predictive control,” Int. J. Robust and Nonlinear Control, 2022, doi: 10.1002/rnc.6532.
    44. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Linear tracking MPC for nonlinear systems part I: the model-based case,” IEEE Trans. Automat. Control, vol. 67, Art. no. 9, 2022, doi: 10.1109/TAC.2022.3166872.
    45. S. Wildhagen, F. Dürr, and F. Allgöwer, “Rollout event-triggered control: reconciling event- and time-triggered control,” at - Automatisierungstechnik, vol. 70, Art. no. 4, 2022, doi: 10.1515/auto-2021-0111.
    46. M. Sharf, A. Koch, D. Zelazo, and F. Allgöwer, “Model-Free Practical Cooperative Control for Diffusively Coupled Systems,” IEEE Transactions on Automatic Control, vol. 67, Art. no. 2, 2022, doi: 10.1109/TAC.2021.3056582.
    47. 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.
    48. 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.
    49. T. Martin and F. Allgöwer, “Dissipativity verification with guarantees for polynomial systems from noisy input-state data,” IEEE Control Systems Lett., vol. 5, Art. no. 4, 2021, doi: 10.1109/LCSYS.2020.3037842.
    50. 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, Art. no. 4, 2021, doi: 10.1109/TAC.2020.3000981.
    51. 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.
    52. 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, Art. no. 4, 2021, doi: 10.1109/TAC.2020.3000182.
    53. 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, Art. no. 7, 2021, doi: 10.1515/auto-2021-0024.
    54. 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, Art. no. 1, 2021, doi: 10.1109/TCNS.2020.3024316.
    55. A. Koch, J. Berberich, J. Köhler, and F. Allgöwer, “Determining optimal input–output properties: A data-driven approach,” Automatica, vol. 134, p. 109906, 2021, doi: https://doi.org/10.1016/j.automatica.2021.109906.
    56. M. I. Müller, A. Koch, F. Allgöwer, and C. R. Rojas, “Data-Driven Input-Passivity Estimation Using Power Iterations,” IFAC-PapersOnLine, vol. 54, Art. no. 7, 2021, doi: https://doi.org/10.1016/j.ifacol.2021.08.429.
    57. P. Pauli, A. Koch, J. Berberich, P. Kohler, and F. Allgöwer, “Training Robust Neural Networks Using Lipschitz Bounds,” IEEE Control Systems Letters, vol. 6, pp. 121–126, 2021, doi: 10.1109/LCSYS.2021.3050444.
    58. S. Yu, M. Hirche, Y. Huang, H. Chen, and F. Allgöwer, “Model predictive control for autonomous ground vehicles: a review,” Auton. Intell. Syst., vol. 1, p. 4, 2021, doi: 10.1007/s43684-021-00005-z.
    59. A. Koch, J. Berberich, and F. Allgöwer, “Provably robust verification of dissipativity properties from data,” IEEE Transactions on Automatic Control, vol. 67, Art. no. 8, 2021, doi: 10.1109/TAC.2021.3116179.
    60. P. Wenzelburger and F. Allgöwer, “Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0,” Applied Sciences, vol. 11, Art. no. 17, 2021, doi: 10.3390/app11178145.
    61. 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.
    62. 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, Art. no. 1, 2020, doi: 10.1038/s41598-020-60400-z.
    63. 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.
    64. 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.
    65. R. Soloperto, J. Köhler, and F. Allgöwer, “Augmenting MPC schemes with active learning: Intuitive tuning and guaranteed performance,” IEEE Control Systems Letters, vol. 4, Art. no. 3, 2020.
    66. 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, Art. no. 2, 2020.
    67. 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, Art. no. 8, 2020.
    68. 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.
    69. 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.
    70. 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.
    71. 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, Art. no. 6, 2020.
    72. S. Wildhagen, M. A. Müller, and F. Allgöwer, “Predictive Control over a Dynamical Token Bucket Network,” IEEE Control Systems Lett., vol. 3, Art. no. 4, 2019, doi: 10.1109/LCSYS.2019.2919264.
    73. 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.
    74. 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.
    75. 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.
    76. 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.
    77. 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.
    78. S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “Event-Based Vehicle Coordination Using Nonlinear Unidirectional Controllers,” IEEE Trans. Control of Network Systems, vol. 5, Art. no. 4, 2018, doi: 10.1109/TCNS.2017.2733959.
    79. 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, Art. no. 1112, 2018, doi: 10.1038/s41419-018-1160-2.
    80. 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, Art. no. 6, 2018, doi: https://doi.org/10.1371/journal.pone.0198203.
    81. M. Hertneck, J. Köhler, S. Trimpe, and F. Allgöwer, “Learning an approximate model predictive controller with guarantees,” IEEE Control Systems Lett., vol. 2, Art. no. 3, 2018, doi: 10.1109/LCSYS.2018.2843682.
    82. D. Imig, K. Kuritz, N. Pollak, M. Rehm, and F. Allgöwer, “Death patterns resulting from cell cycle-independent cell death,” IFAC-PapersOnLine, vol. 51, Art. no. 19, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.09.028.
    83. 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, Art. no. 9, 2018.
    84. 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, Art. no. 4, 2018, doi: 10.1109/lcsys.2018.2847449.
    85. 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.
    86. J. Köhler, M. A. Müller, and F. Allgöwer, “On periodic dissipativity notions in economic model predictive control,” IEEE Control Systems Letters, vol. 2, Art. no. 3, 2018.
    87. 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.
    88. K. Kuritz, D. Imig, M. Dyck, and F. Allgöwer, “Ensemble control for cell cycle synchronization of heterogeneous cell populations,” IFAC-PapersOnLine, vol. 51, Art. no. 19, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.09.034.
    89. K. Kuritz, S. Zeng, and F. Allgöwer, “Ensemble Controllability of Cellular Oscillators,” IEEE Control Systems Letters, vol. 3, Art. no. 2, 2018, doi: 10.1109/LCSYS.2018.2870967.
    90. 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, Art. no. 3, 2018, doi: 10.1109/LCSYS.2018.2842142.
    91. 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.
    92. 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.
    93. 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.
    94. 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.
    95. 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, Art. no. 2, 2017.
    96. G. Goebel and F. Allgöwer, “New results on semi-explicit and almost explicit MPC algorithms,” at-Automatisierungstechnik, vol. 65, Art. no. 4, 2017.
    97. G. Goebel and F. Allgöwer, “Semi-explicit MPC based on subspace clustering,” Automatica, vol. 83, pp. 309–316, 2017.
    98. 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.
    99. M. Lorenzen, F. Dabbene, R. Tempo, and F. Allgöwer, “Constraint-Tightening and Stability in Stochastic Model Predictive Control,” IEEE Trans. Automat. Control, vol. 62, Art. no. 7, 2017, doi: 10.1109/TAC.2016.2625048.
    100. 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.
    101. 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.
    102. C. Thomaseth, K. Kuritz, F. Allgoewer, and R. N., “The circuit-breaking algorithm for monotone systems,” Mathematical Biosciences, vol. 284, pp. 80–91, 2017.
    103. 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.
    104. J. M. Montenbruck, M. Arcak, and F. Allgöwer, “An Input-Output Framework for Submanifold Stabilization,” IEEE Trans. Automat. Control, vol. 62, Art. no. 10, 2017.
    105. J. M. Montenbruck, D. Zelazo, and F. Allgöwer, “Fekete Points, Formation Control, and the Balancing Problem,” IEEE Trans. Automat. Control, vol. 62, Art. no. 10, 2017.

  2. Conferences

    1. Yifan Xie, Julian Berberich, Frank Allgöwer, “Data-Driven Min-Max MPC for Linear Systems,” 2024, pp. 3184–3189.

  3. Contributions to anthologies

    1. 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.

  4. Conference papers

    1. F. Brändle, D. Meister, M. Seidel, R. Strässer, and F. Allgöwer, “On the effects of angular acceleration in orientation estimation using inertial measurement units,” in submitted, Preprint: arxiv: 2502.03681, 2025. [Online]. Available: https://arxiv.org/abs/2502.03681
    2. D. Meister et al., “Localization and path following for an autonomous e-scooter,” in submitted, 2025. [Online]. Available: https://arxiv.org/abs/2505.05314
    3. R. Strässer, F. Brändle, D. Meister, M. Seidel, and F. Allgöwer, “Autonomous E-Scooters for Sustainable Urban Mobility: Achievements and Insights from an Experimental Prototype,” in European Robotics Forum 2025, Cham: Springer Nature Switzerland, 2025, pp. 3–9. doi: 10.1007/978-3-031-89471-8_1.
    4. M. Eyüboglu, R. Strässer, F. Allgöwer, and A. Karimi, “Koopman-based LPV control: A data-driven approach using IQCs,” in submitted, 2025.
    5. R. Strässer, J. Berberich, and F. Allgöwer, “Koopman-based control using sum-of-squares optimization: Improved stability guarantees and data efficiency,” in Proc. European Control Conference (ECC), Preprint: arxiv:2411.03875, 2025.
    6. F. Brändle and F. Allgöwer, “Data-driven Estimator Synthesis with Instantaneous Noise,” in submitted, Preprint: arxiv: 2504.08299, 2025. [Online]. Available: https://arxiv.org/abs/2504.08299
    7. K. Worthmann, R. Strässer, M. Schaller, J. Berberich, and F. Allgöwer, “Data-driven MPC with terminal conditions in the Koopman framework,” in Proc. 63rd IEEE Conference on Decision and Control (CDC), Milan, Italy, 2024, pp. 146–151. doi: 10.1109/CDC56724.2024.10886773.
    8. R. Strässer et al., “Collision Avoidance Safety Filter for an Autonomous E-Scooter using Ultrasonic Sensors,” in Proc. 17th IFAC Symposium on Control in Transportation Systems (CTS 2024), 2024, pp. 22–28. doi: 10.1016/j.ifacol.2024.07.313.
    9. M. Hertneck, A. I. Maass, D. Nesić, and F. Allgöwer, “An $L_p$-norm framework for event-triggered control,” in Proc. European Control Conf. (ECC), Stockholm, Sweden, 2024, pp. 2818–2824. doi: 10.23919/ECC64448.2024.10591119.
    10. J. Venkatasubramanian, J. Köhler, M. Cannon, and F. Allgöwer, “Towards targeted exploration for non-stochastic disturbances,” in Proc. 20th IFAC Symposium on System Identification SYSID 2024, Boston, USA, 2024. doi: 10.1016/j.ifacol.2024.08.588.
    11. F. Brändle and F. Allgöwer, “A System Parametrization for Direct Data-Driven Analysis and Control with Error-in-Variables,” in submitted, Preprint: arxiv: 2411.06787, 2024. [Online]. Available: https://arxiv.org/abs/2411.06787
    12. F. Brändle and F. Allgöwer, “A System Parameterization for Direct Data-Driven Estimator Synthesis,” in submitted, Preprint: arxiv: 2410.10681, 2024. [Online]. Available: https://arxiv.org/abs/2410.10681
    13. M. Hertneck and F. Allgöwer, “Reverse average dwell time constraints enable arbitrary maximum allowable transmission intervals,” in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS), Canberra, Australia, 2023, pp. 379–384. doi: 10.1016/j.ifacol.2023.02.064.
    14. H. Schlüter and F. Allgöwer, “Stochastic Model Predictive Control using Initial State and Variance Interpolation,” in Proc. 62nd IEEE Conference on Decision and Control (CDC), Singapore: IEEE, Dec. 2023. doi: 10.1109/CDC49753.2023.10383711.
    15. J. Berberich, A. Iannelli, A. Padoan, J. Coulson, F. Dörfler, and F. Allgöwer, “A quantitative and constructive proof of Willems’ Fundamental Lemma and its implications,” in Proc. American Control Conf. (ACC), San Diego, CA, USA, 2023, pp. 4155–4160. doi: 10.23919/ACC55779.2023.10156227.
    16. T. Martin, T. B. Schön, and F. Allgöwer, “Gaussian inference for data-driven state-feedback design of nonlinear systems,” in 22nd IFAC World Congress, 2023, pp. 4796–4803. doi: doi.org/10.1016/j.ifacol.2023.10.1245.
    17. R. Strässer, J. Berberich, and F. Allgöwer, “Robust data-driven control for nonlinear systems using the Koopman operator,” in Proc. 22nd IFAC World Congress, 2023, pp. 2257–2262. doi: https://doi.org/10.1016/j.ifacol.2023.10.1190.
    18. S. Schlor, R. Strässer, and F. Allgöwer, “Koopman interpretation and analysis of a public-key cryptosystem: Diffie-Hellman key exchange,” in Proc. 22nd IFAC World Congress, Yokohama, Japan, 2023, pp. 984–990. doi: 10.1016/j.ifacol.2023.10.1693.
    19. R. Strässer, J. Berberich, and F. Allgöwer, “Control of bilinear systems using gain-scheduling: Stability and performance guarantees,” in Proc. 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 4674–4681. doi: 10.1109/CDC49753.2023.10384021.
    20. M. Alsalti, V. G. Lopez, J. Berberich, F. Allgöwer, and M. A. Müller, “Data-driven nonlinear predictive control for feedback linearizable systems,” in Proc. 22nd IFAC World Congress, Yokohama, Japan, 2023, pp. 617–624. doi: 10.1016/j.ifacol.2023.10.1636.
    21. D. Meister, F. Dürr, and F. Allgöwer, “Shared Network Effects in Time- versus Event-Triggered Consensus of a Single-Integrator Multi-Agent System,” in 22nd IFAC World Congress, Yokohama, Japan, 2023, pp. 5975–5980. doi: 10.1016/j.ifacol.2023.10.636.
    22. M. Hertneck and F. Allgöwer, “Self-triggered output feedback control for nonlinear networked control systems based on hybrid Lyapunov functions,” in Proc. 22nd IFAC World Congress, Tokyo, Japan, 2023, pp. 5748–5753. doi: 10.1016/j.ifacol.2023.10.165.
    23. P. Pauli, D. Gramlich, and F. Allgöwer, “Lipschitz constant estimation for 1D convolutional neural networks,” in Proc. 5th Annual Learning for Dynamics and Control Conf. (L4DC), in Proceedings of Machine Learning Research, vol. 211. Philadelphia, PA, USA: PMLR, 2023, pp. 1321–1332.
    24. Z. Ma, H. Schlüter, F. Berkel, T. Specker, and F. Allgöwer, “Recursive Feasibility and Stability for Stochastic MPC based on Polynomial Chaos,” in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS), Canberra, Australia: Elsevier, Jan. 2023, pp. 204–209. doi: 10.1016/j.ifacol.2023.02.035.
    25. D. Meister and F. Allgöwer, “Performance implications of different p-norms in level-triggered sampling,” in Proc. 62nd IEEE Conf. on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 3878–3883. doi: 10.1109/CDC49753.2023.10384009.
    26. D. Antunes, D. Meister, T. Namerikawa, F. Allgöwer, and W. P. M. H. Heemels, “Consistent event-triggered consensus on complete graphs,” in Proc. 62nd IEEE Conf. on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 3911–3916. doi: 10.1109/CDC49753.2023.10384026.
    27. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Robust peak-to-peak gain analysis using integral quadratic constraints,” in Proc. 22nd IFAC World Congress, Yokohama, Japan, 2023, pp. 11564–11569. doi: 10.1016/j.ifacol.2023.10.452.
    28. T. Martin and F. Allgöwer, “Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation,” in Proc. American Control Conf. (ACC), Atlanta, GA, USA, 2022, pp. 1432–1437. doi: 10.23919/ACC53348.2022.9867806.
    29. M. Köhler, J. Berberich, M. A. Müller, and F. Allgöwer, “Data-driven distributed MPC of dynamically coupled linear systems,” in Proc. 25th Int. Symp. Math. Theory Netw. Syst. (MTNS), Bayreuth, Germany, 2022, pp. 365–370. doi: 10.1016/j.ifacol.2022.11.080.
    30. H. Schlüter and F. Allgöwer, “Stochastic model predictive control using initial state optimization,” in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems (MTNS), Bayreuth, Germany: Elsevier, Nov. 2022, pp. 454–459. doi: 10.1016/j.ifacol.2022.11.095.
    31. D. Meister, F. Aurzada, M. A. Lifshits, and F. Allgöwer, “Analysis of Time- versus Event-Triggered Consensus for a Single-Integrator Multi-Agent System,” in Proc. 61st IEEE Conf. on Decision and Control (CDC), Cancun, Mexico, 2022, pp. 441–446. doi: 10.1109/CDC51059.2022.9993301.
    32. P. Pauli, N. Funcke, D. Gramlich, M. A. Msalmi, and F. Allgöwer, “Neural network training under semidefinite constraints,” in 2022 IEEE 61st Conference on Decision and Control (CDC), Dec. 2022, pp. 2731–2736. doi: 10.1109/CDC51059.2022.9992331.
    33. S. Wildhagen, M. Pezzutto, L. Schenato, and F. Allgöwer, “Self-triggered MPC robust to bounded packet loss via a min-max approach,” in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022, pp. 7670–7675. doi: 10.1109/CDC51059.2022.9992581.
    34. M. Hertneck and F. Allgöwer, “Dynamic self-triggered control for nonlinear systems with delays,” in Proc. 9th IFAC Conf. on Networked Systems (NECSYS), Zürich, Switzerland, 2022, pp. 312–317. doi: 10.1016/j.ifacol.2022.07.278.
    35. R. Drummond, S. Duncan, M. Turner, P. Pauli, and F. Allgower, “Bounding the difference between model predictive control and neural networks,” in Learning for Dynamics and Control Conference, PMLR, 2022, pp. 817–829.
    36. D. Müller, J. Feilhauer, J. Wickert, J. Berberich, F. Allgöwer, and O. Sawodny, “Data-driven predictive disturbance observer for quasi continuum manipulators,” in Proc. 61st IEEE Conf. Decision and Control (CDC), Cancun, Mexico, 2022, pp. 1816–1822. doi: 10.1109/CDC51059.2022.9992740.
    37. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Stability in data-driven MPC: an inherent robustness perspective,” in Proc. 61st IEEE Conf. Decision and Control (CDC), Cancun, Mexico, 2022, pp. 1105–1110. doi: 10.1109/CDC51059.2022.9993361.
    38. P. Pauli, D. Gramlich, J. Berberich, and F. Allgöwer, “Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers,” in Proc. 60th IEEE Conf. on Decision and Control (CDC), Austin, TX, USA, 2021, pp. 3611–3618.
    39. P. Pauli, J. Köhler, J. Berberich, A. Koch, and F. Allgöwer, “Offset-free setpoint tracking using neural network controllers,” in Proc. 3rd Conf. on Learning for Dynamics and Control (L4DC), Zurich, Switzerland, 2021, pp. 992–1003.
    40. A. Alanwar, A. Koch, F. Allgöwer, and K. H. Johansson, “Data-Driven Reachability Analysis Using Matrix Zonotopes,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research, vol. 144. 2021, pp. 163–175.
    41. M. Hertneck and F. Allgöwer, “A Simple Approach to Increase the Maximum Allowable Transmission Interval,” in Proc. 3rd IFAC Conf. on Modelling, Identification and Control of Nonlinear Systems (MICNON), Tokyo, Japan, 2021, pp. 443–448. doi: 10.1016/j.ifacol.2021.10.390.
    42. N. Wieler, J. Berberich, A. Koch, and F. Allgöwer, “Data-Driven Controller Design via Finite-Horizon Dissipativity,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research, vol. 144. PMLR, 2021, pp. 287–298.
    43. R. Soloperto, P. Wenzelburger, D. Meister, D. Scheuble, V. S. M. Breidohr, and F. Allgöwer, “A control framework for autonomous e-scooters,” in Proc. 16th IFAC Symposium on Control in Transportation Systems (CTS), Lille, France, 2021, pp. 252–258. doi: 10.1016/j.ifacol.2021.06.030.
    44. J. Berberich, S. Wildhagen, M. Hertneck, and F. Allgöwer, “Data-driven analysis and control of continuous-time systems under aperiodic sampling,” in Proc. 19th IFAC Symp. System Identification (SYSID), Padova, Italy, 2021, pp. 210–215. doi: 10.1016/j.ifacol.2021.08.360.
    45. C. Klöppelt, L. Schwenkel, F. Allgöwer, and M. A. Müller, “Transient Performance of Tube-based Robust Economic Model Predictive Control,” in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), Bratislava, Slovakia, 2021, pp. 28–35. doi: 10.1016/j.ifacol.2021.08.520.
    46. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “On the design of terminal ingredients for data-driven MPC,” in Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC), Bratislava, Slovakia, 2021, pp. 257–263. doi: 10.1016/j.ifacol.2021.08.554.
    47. M. Alsalti, J. Berberich, V. G. Lopez, F. Allgöwer, and M. A. Müller, “Data-Based System Analysis and Control of Flat Nonlinear Systems,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 1484–1489. doi: 10.1109/CDC45484.2021.9683327.
    48. S. Schlor, M. Hertneck, S. Wildhagen, and F. Allgöwer, “Multi-party computation enables secure polynomial control based solely on secret-sharing,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 4882–4887. doi: 10.1109/CDC45484.2021.9683026.
    49. M. Hertneck and F. Allgöwer, “Dynamic self-triggered control for nonlinear systems based on hybrid Lyapunov functions,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 533–539. doi: 10.1109/CDC45484.2021.9682784.
    50. S. Wildhagen, J. Berberich, M. Hirche, and F. Allgöwer, “Improved stability conditions for systems under aperiodic sampling: model- and data-based analysis,” in Proc. 60th IEEE Conf. on Decision and Control (CDC), Austin, TX, USA, 2021, pp. 5788–5794.
    51. R. Strässer, J. Berberich, and F. Allgöwer, “Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 4344–4351. doi: 10.1109/CDC45484.2021.9683211.
    52. J. Venkatasubramanian, J. Köhler, J. Berberich, and F. Allgöwer, “Robust dual control based on gain scheduling,” in 2020 59th IEEE Conference on Decision and Control (CDC), IEEE, 2021, pp. 2270–2277. doi: 10.1109/CDC42340.2020.9304336.
    53. E. Müller, P. N. Köhler, K. Y. Pettersen, and F. Allgöwer, “Economic model predictive control for obstacle-aided snake robot locomotion,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020.
    54. P. Pauli, A. Koch, and F. Allgöwer, “Smartphone Apps for Learning Progress and Course Revision,” in Proc.\ 21st IFAC World Congress, Berlin, Germany, Jul. 2020.
    55. 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.
    56. 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.
    57. 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.
    58. 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.
    59. 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.
    60. 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.
    61. 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.
    62. 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. doi: 10.1109/CDC40024.2019.9029770.
    63. 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. on Decision and Control (CDC), Jeju Island, Republic of Korea, 2020, pp. 1248–1253. doi: 10.1109/CDC42340.2020.9303838.
    64. 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.
    65. L. Schwenkel, J. Köhler, M. A. Müller, and F. Allgöwer, “Robust Economic Model Predictive Control without Terminal Conditions,” in Proc. of 21st IFAC World Congress, 2020. doi: 10.1016/j.ifacol.2020.12.465.
    66. 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.
    67. A. Camisa, P. N. Köhler, M. A. Müller, G. Notarstefano, and F. Allgöwer, “A distributed optimization algorithm for Nash bargaining in multi-agent systems,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020.
    68. 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), Jeju, South Korea, 2020, pp. 1235–1241. doi: 10.1109/CDC42340.2020.9303819.
    69. 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.
    70. 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.
    71. 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.
    72. 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.
    73. M. Rosenfelder, J. Köhler, and F. Allgöwer, “Stability and performance in transient average constrained economic MPC without terminal constraints,” in Proc.\ 21st IFAC World Congress, 2020.
    74. 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.
    75. 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.
    76. 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.
    77. P. Wenzelburger and F. Allgöwer, “A first step towards an autonomously driving E-Scooter,” in Demonstrator Session 21st IFAC World Congress, Berlin, Germany, 2020. [Online]. Available: https://www.ist.uni-stuttgart.de/institute/team/pdf/PW/IFAC20_E-Scooter.pdf
    78. 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.
    79. 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, Jul. 2020, pp. 7130–7135. doi: 10.1016/j.ifacol.2020.12.518.
    80. 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.
    81. S. Wildhagen, C. N. Jones, and F. Allgöwer, “A resource-aware approach to self-triggered model predictive control,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020. doi: 10.1016/j.ifacol.2020.12.926.
    82. 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.
    83. R. Soloperto, J. Köhler, M. A. Müller, and F. Allgöwer, “Dual Adaptive MPC for output tracking of linear systems,” in Proc. 58th Conference on Decision and Control (CDC), Nice, France, 2019.
    84. 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.
    85. 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.
    86. 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.
    87. 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.
    88. 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.
    89. 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.
    90. 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.
    91. 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.
    92. 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.
    93. 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.
    94. W. Halter, S. Michalowsky, and F. Allgöwer, “Extremum seeking for optimal enzyme production under cellular fitness constraints,” in Proc. European Control Conf. (ECC), Neapel, Italien, 2019.
    95. 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.
    96. 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.
    97. 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.
    98. M. Nonhoff, P. N. Köhler, and F. Allgöwer, “Economic model predictive control for snake robot locomotion,” in Proc. 58th IEEE Conference on Decision and Control (CDC), Nice, France, 2019.
    99. R. Soloperto, J. Köhler, M. A. Müller, and F. Allgöwer, “Collision avoidance for uncertain nonlinear systems and moving obstacles using robust Model Predictive Control,” in Proc. 18th European Control Conference (ECC), Naples, Italy, 2019.
    100. 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.
    101. 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.
    102. 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.
    103. 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.
    104. W. Halter, F. Allgöwer, R. M. Murray, and A. Gyorgy, “Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts,” in Proc. 57th IEEE Conf. Decision and Control (CDC), Miami Beach, USA, 2018.
    105. 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.
    106. 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.
    107. 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.
    108. R. Soloperto, M. A. Müller, and F. Allgöwer, “Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty,” in Proc. 6th IFAC Conference on Nonlinear Model Predictive Control, Madison, Wisconsin, 2018.
    109. 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.
    110. 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.
    111. 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.
    112. 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.
    113. 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.
    114. 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.
    115. W. Halter, Z. A. Tuza, and F. Allgöwer, “Signal differentiation with genetic networks,” in Proc. 20th IFAC World Congress, Toulouse, France, 2017.
    116. 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.
    117. 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.
    118. 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.
    119. 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.
    120. W. Halter, J. M. Montenbruck, and F. Allgöwer, “Systems with integral resource consumption,” in Proc. 56th IEEE Conf. Decision and Control (CDC), Melbourne, Australia, 2017.
    121. 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.
    122. 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.
    123. 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.

  5. Preprints

    1. Y. Wei et al., “Robust Data-driven Min-max Model Predictive Control with Unknown-input Observers,” submitted, 2025.
    2. L. Schwenkel, D. Briem, M. A. Müller, and F. Allgöwer, “On discount functions for economic model predictive control without terminal conditions,” arXiv preprint arXiv:2405.14361, 2024.

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, optimization-based control, networks of systems, and systems biology. Frank received several recognitions 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).

Google Profil                                      List of publications

1981 – 1987       Diploma studies in Engineering Cybernetics and Applied
                           Mathematics at University of Stuttgart and University of California
                           at Los Angeles (UCLA)

1988 – 1995       Research Associate at Institute for System Dynamics and
                           Automatic Control, University of Stuttgart

1991 – 1992       Visiting Research Associate, California Institute of Technology,
                           Pasadena, CA, USA

1995 – 1996       Visiting Research Associate, DuPont Experimental Station,
                           Wilmington, DE, USA

1996                   Ph.D., University of Stuttgart, advisor: Prof. E.D. Gilles

1996                   Offer of University of California at Berkeley (tenure-track assistant
                           professorship)  

1996 – 1999       Assistant Professor for Nonlinear Systems, Electrical Engineering
                           Department, ETH Zürich, Switzerland

1998                   Offer of University of Duisburg (Full Professor of Automatic
                           Control)

1999                   Offer of ETH Zürich (Full Professor of Automatic Control)

since 1999          Full Professor and Director of the Institute for Systems Theory
                            and Automatic Control, University of Stuttgart

2003 – 2004        Visiting Professor, University of California at Santa Barbara, CA,
                            USA

2007                    Offer of University of California at Santa Barbara (Endowed Chair,
                            Electrical Engineering)

2010 – 2011         Visiting Professor, University of Newcastle, Australia

2012 – 2020         Vice-president of the German Research Foundation (DFG)

2018                     Co-founder, Spin-off company TGU Systemwissenschaften

2019                     Co-founder, Spin-off company eStarling.io

 

2002      NaT-Working Award of the Robert Bosch Foundation

2004      Gottfried-Wilhelm-Leibniz Prize of the German Research Foundation

2005      Best Paper Award 2004/2005, Asian Journal of Control

2005      D.B. Robinson Distinguished Speaker (September 29, 2005), University
              of Alberta, Edmonton

2006      IEEE Distinguished Lecturer

2006      Fellow, International Federation of Automatic Control (IFAC)

2007      State Teaching Award of the state of Baden-Württemberg

2008      Best Paper Award, Journal Control Engineering Practice

2009      Best Poster Award, Cancer Systems Biology 2009

2011       Best Paper Award, 8th International Workshop on Computational
               Systems Biology

2011       Outstanding Service Award, International Federation of Automatic Control

2011       International Best Paper Award, SICE 2011

2013       Best Paper Award 2012/2013, Asian Journal of Control

2013       DeGruyter Publishing Best Paper Award

2015       Distinguished Member Award of the IEEE Control System Society

2017       Journal of Process Control Paper Prize Award

2017       24th Roger Sargent Lecture, December 9, 2017, Imperial College London

2018       Publication Prize 2017, University of Stuttgart

2018       Best Paper Award, 9th IFAC Symposium on Robust Control Design

2019       Best Paper Award, 8th IFAC Workshop on Distributed Estimation and
               Control in Networked Systems (NecSys 2019)

2019       Winner „Ideenwettbewerb: Mobilitätskonzepte für den emissionsfreien
               Campus“ MWK Baden-Württemberg for the initiative Mobility Living Lab
               (MobiLab)

2020       Outstanding Student Paper Award for "Robust Constraint Satisfaction in
               Data-Driven MPC" (J. Berberich, J. Köhler, M.A. Müller and F.  Allgöwer)
               at the IEEE CDC Conference 2020.

2021       Publication Prize 2019, University of Stuttgart

More than 20 invitations for plenary and keynote talks at international conferences in the past five years

Winter term 2021/2022

Summer term 2021

 

 

Name

School

Year

Descendants

Berberich, Julian

Universität Stuttgart

2022

 

Wenzelburger, Philipp

Universität Stuttgart

2022

 

Linsenmayer, Steffen

Universität Stuttgart

2021

 

Köhler, Johannes

Universität Stuttgart

2021

  

Löhning, Martin

Universität Stuttgart

2021

 

Halter, Wolfgang

Universität Stuttgart

2020

 

Imig, Dirke

Universität Stuttgart

2020

 

Köhler, Philipp

Universität Stuttgart

2020

 

Kuritz, Karsten

Universität Stuttgart

2020

 

Lorenzen, Matthias

Universität Stuttgart

2018

 

Bayer, Florian

Universität Stuttgart

2017

 

Brunner, Florian

Universität Stuttgart

2017

 

Goebel, Gregor

Universität Stuttgart

2017

 

Wu, Jingbo

Universität Stuttgart

2017

 

Montenbruck, Jan-Maximilian

Universität Stuttgart

2016

 

Seyboth, Georg

Universität Stuttgart

2016

 

Zeng, Shen-Shen

Universität Stuttgart

2016

 

Breindl, Christian

Universität Stuttgart

2015

 

Schittler, Daniella

Universität Stuttgart

2015

 

Müller, Matthias

Universität Stuttgart

2014

 

Schmidt, Gerd

Universität Stuttgart

2014

 

Schuler, Simone

Universität Stuttgart

2014

 

Blind, Rainer

Universität Stuttgart

2013

 

Bürger, Mathias

Universität Stuttgart

2013

 

Hasenauer, Jan

Universität Stuttgart

2013

 

Reble, Marcus

Universität Stuttgart

2013

 

Böhm, Christoph

Universität Stuttgart

2011

 

Yu, Shuyou

Universität Stuttgart

2011

 

Münz, Ulrich

Universität Stuttgart

2010

 

Raff, Tobias

Universität Stuttgart

2010

 

Wieland, Peter

Universität Stuttgart

2010

 

Assfalg, Jochen

Universität Stuttgart

2009

 

Johannes, Maess

Universität Stuttgart

2009

 

Waldherr, Steffen

Universität Stuttgart

2009

 

Eissing, Thomas

Universität Stuttgart

2007

 

Rieber, Jochen

Universität Stuttgart

2006

 

Schweickhardt, Tobias

Universität Stuttgart

2006

 

Ebenbauer, Christian

Universität Stuttgart

2005

 

Findeisen, Rolf

Universität Stuttgart

2004

1

Menold, Patrick

Universität Stuttgart

2004

 

Schitter, Georg

ETH Zürich

2004

 

Rehm, Ansgar

Universität Stuttgart

2003

 

Bullinger, Eric

ETH Zürich

2001

1

Chen, Hong

Universität Stuttgart

1997

 

Development of new methods for system and control theory with main focus on:

  • Nonlinear Control
  • Networked Control
  • Predictive Control
  • Data-based Control

Application focuses:

  • Process Control
  • Mechatronics
  • Biomedical Technology
  • Nanotechnology
  • Systems Biology

Universities

since 1999      Dean of Academic Affairs, Engineering Cybernetics, University of
                       Stuttgart

1999 – 2004   Deputy spokesperson, Collaborative Research Center SFB 412

2003 – 2012   Member, Review board Systems Engineering, DFG

2005 – 2013   Member, Governing Board Center for Systems Biology, University of
                       Stuttgart

since 2006     Liaison professor of the German Academic Scholarship Foundation

2006 – 2012   Member, Senate Structure Committee, University of Stuttgart

2006 – 2012   Member, “Ehrungskommission” (university-wide awards
                       committee), University of Stuttgart

seit 2007         Member of the Executive Board, Exzellence Cluster „Simulation
                       Technology” (2007-2018) and „Data-integrated Simulation Science“
                       (since 2019), University of Stuttgart

seit 2009         Chair, Graduate School „Simulation Technology”, University of
                        Stuttgart

seit 2009         Chair, Doctoral Grants Committee, Stuttgart Center for Simulation
                        Science

2009 – 2013    Member of the Management Board, Informatik Verbund Stuttgart

2012 – 2017    Member, Selection Committee for the Gottfried Wilhelm Leibniz
                        Programme, DFG

2012 – 2017    Chairman of the Jury, Communicator Award, Stifterverband and
                        DFG

2013               „Sounding Board“ Ingenieurwissenschaften@2025 of the Ministry
                        for Science and the Arts, Baden-Württemberg

2013 – 2016    Member of the Advisory Committee „Core data set science“
                        (Kerndatensatz), German Council of Science and Humanties
                        (Wissenschaftsrat)

2014 – 2018     Executive Director, Stuttgart Research Centre Systems Biology

2014 – 2015    Member of the Steering Group, Internationalen Research Training
                        Groups of the DFG 

2014 – 2020    Chairman, des Gemeinsamen Ausschuss für Sicherheitsrelevante
                        Forschung von Leopoldina und DFG

2015 – 2020    Spokesperson, Research Alliance „System Mensch“ of the
                        Universities of  Stuttgart and Tübingen

since 2017      Deputy Spokesperson, International Max Planck Research School
                        for Intelligent Systems , Stuttgart and Tübingen

2018 – 2020    Member, Expert Committee „Wissenschaft im digitalen Zeitalter“,
                        DFG

since 2018       Member, Cyber Valley Plenary Assembly

since 2019       Deputy Spokesperson, Excellence Cluster „Data-integrated
                        simulation science“

since 2019       Co-Director, Stuttgart Center for Simulation Science, University of
                        Stuttgart

since 2019       Member, Cyber Valley Research Fund Boards

2019 – 2020    Chairman, German National Research Data Infrastructure (NFDI)
                        Expert Committee

since 2020       Member, Interdisciplinary Commission for Pandemic Research,
                        DFG

since 2020       Founding Member, Interchange Forum for the Reflection of
                         Intelligent Systems

 

Scientific Organizations

2000 – 2015    Member of the Board, VDI/VDE-Gesellschaft Mess- und
                        Automatisierungstechnik (GMA)

2000 – 2015    Chairman, Fachbereich 1 „Grundlagen und Methoden der Mess-
                        und Automatisierungstechnik“ of the VDI/VDE-Gesellschaft Mess-
                        und Automatisierungstechnik (GMA)

2001 – 2008    Chairman, Technical Committee on Nonlinear Systems, International
                        Federation of Automatic Control (IFAC)

2001 – 2004    Council Member, European Union Control Association (EUCA)

2005 – 2008    Member, Policy Committee, Intern. Federation of Automatic
                        Control (IFAC)

2005 – 2008    Member, Board of Governors, IEEE Control Systems Society

2007 – 2012    Chairman, International Affairs Committee, IEEE Control Systems
                        Society

since 2008      Council Member, International Federation of Automatic Control
                       (IFAC)

2011 – 2014    Member, Board of Governors, IEEE Control Systems Society

2011 – 2015    Chairman, Strategic Planning Group, IFAC

2011 – 2016    Member, IEEE Life Sciences New Initiative (LSNI) Project Team

2012 – 2015    Member, Long Range Planning Committee, IEEE Control Systems
                        Society

2013 – 2014    Vice-president for Technical Activities, IEEE Control Systems
                        Society

2014 – 2017    Chairman, Administration & Finance Committee, IFAC

2014 – 2017    Member, IEEE Control Systems Award Committee

2014 – 2017    Chairman, Election Committee, IFAC

2017 – 2020    President, International Federation of Automatic Control (IFAC)

since 2017       Member, IFAC Publication Management Board

since 2020       Chairman, Membership Committee, IFAC


Editorial Activities

1997 – 2001     Associate Editor, Automatica (Elsevier)

1997 – 2008     Associate Editor, Journal of Process Control (Elsevier)

2001 – 2015     Editor, Automatica, Process and Computer Control Area (Elsevier)

2003 – 2007     Associate Editor, European Journal of Control (Hermes Science)

since 2008       Editor-in-Chief, Springer Lecture Notes in Control and Information
                        Sciences

2010 – 2016     Associate Editor, IMA Journal of Mathematical Control and
                         Information

since 1998        Member of various advisory boards for international journals
                          including IEE Proceedings on Control Theory and Applications
                         (since 2006 IET), Journal of Nonlinear and Robust Control,
                         Chemical Engineering Science, Canadian Journal of Chemical
                         Engineering, Control Handbook (CRC Press)

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