Dieses Bild zeigt Frank Allgöwer

Frank Allgöwer

Herr Prof. Dr.-Ing.

Institutsleiter
Institut für Systemtheorie und Regelungstechnik

Kontakt

Pfaffenwaldring 9
70569 Stuttgart
Germany
Raum: 2.246

Sprechstunde

Nach Vereinbarung.

Sekretariat:
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. (Zeitschriften-) Aufsätze

    1. L. Schwenkel, A. Hadorn, M. A. Müller, und F. Allgöwer, „Linearly discounted economic MPC without terminal conditions for periodic optimal operation“, Automatica, Bd. 159, S. 111393, 2024, doi: 10.1016/j.automatica.2023.111393.
    2. K. Worthmann, R. Strässer, M. Schaller, J. Berberich, und F. Allgöwer, „Data-driven MPC with terminal conditions in the Koopman framework“, submitted, 2024.
    3. M. Hertneck und F. Allgöwer, „Robust dynamic self-triggered control for nonlinear systems using hybrid Lyapunov functions“, Nonlinear Analysis: Hybrid Systems, Bd. 53, S. 101485, 2024, doi: 10.1016/j.nahs.2024.101485.
    4. S. Schlor und F. Allgöwer, „Bootstrapping Guarantees: Stability and Performance Analysis for Dynamic Encrypted Control“, submitted to L-CSS, Preprint: arxiv:2403.18571, 2024.
    5. D. Meister, F. Aurzada, M. A. Lifshits, und F. Allgöwer, „Time- versus event-triggered consensus of a single-integrator multi-agent system“, Nonlinear Analysis: Hybrid Systems, Bd. 53, S. 101494, 2024, doi: 10.1016/j.nahs.2024.101494.
    6. M. Seidel, M. Hertneck, P. Yu, S. Linsenmayer, D. V. Dimarogonas, und F. Allgöwer, „A Window-based Periodic Event-triggered Consensus Scheme for Multi-agent Systems“, IEEE Transactions on Control of Network Systems, Bd. 11, Nr. 1, Art. Nr. 1, März 2024, doi: 10.1109/tcns.2023.3285863.
    7. L. Schwenkel, J. Köhler, M. A. Müller, und F. Allgöwer, „Model predictive control for linear uncertain systems using integral quadratic constraints“, IEEE Trans. Automat. Control, Bd. 68, Nr. 1, Art. Nr. 1, 2023, doi: 10.1109/TAC.2022.3171410.
    8. J. Bongard, J. Berberich, J. Köhler, und F. Allgöwer, „Robust stability analysis of a simple data-driven model predictive control approach“, IEEE Trans. Automat. Control, Bd. 68, Nr. 5, Art. Nr. 5, 2023, doi: 10.1109/TAC.2022.3163110.
    9. V. Wagner, R. Strässer, F. Allgöwer, und 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, Bd. 19, Nr. 24, Art. Nr. 24, Dez. 2023, doi: https://doi.org/10.1021/acs.jctc.3c00548.
    10. R. Strässer, S. Schlor, und F. Allgöwer, „Decrypting Nonlinearity: Koopman Interpretation and Analysis of Cryptosystems“, submitted, Preprint: arxiv:2311.12714, 2023.
    11. T. Martin, T. B. Schön, und F. Allgöwer, „Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey“, Annual Reviews in Control, Bd. 56, S. 100911, 2023, doi: 10.1016/j.arcontrol.2023.100911.
    12. A. Alanwar, A. Koch, F. Allgöwer, und F. H. Johansson, „Data-Driven Reachability Analysis from Noisy Data“, IEEE Transactions on Automatic Control, S. 1–16, 2023, doi: 10.1109/TAC.2023.3257167.
    13. T. Martin und F. Allgöwer, „Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures“, IEEE Trans. Automat. Control, Bd. 68, Nr. 5, Art. Nr. 5, 2023, doi: 10.1109/TAC.2022.3226652.
    14. J. Berberich, C. W. Scherer, und F. Allgöwer, „Combining prior knowledge and data for robust controller design“, IEEE Trans. Automat. Control, Bd. 68, Nr. 8, Art. Nr. 8, 2023, doi: 10.1109/TAC.2022.3209342.
    15. X. Wang, J. Sun, J. Berberich, G. Wang, F. Allgöwer, und J. Chen, „Data-driven Control of Dynamic Event-triggered Systems with Delays“, Int. J. Robust and Nonlinear Control, Bd. 33, S. 7071–7093, 2023, doi: 10.1002/rnc.6740.
    16. M. Köhler, L. Krügel, L. Grüne, M. A. Müller, und F. Allgöwer, „Transient Performance of MPC for Tracking“, IEEE Control Systems Letters, Bd. 7, S. 2545–2550, 2023, doi: 10.1109/LCSYS.2023.3287798.
    17. P. N. Köhler, M. A. Müller, und F. Allgöwer, „Approximate Dissipativity of Cost-Interconnected Systems in Distributed Economic MPC“, IEEE Transactions on Automatic Control, Bd. 68, Nr. 4, Art. Nr. 4, 2023, doi: 10.1109/TAC.2022.3173028.
    18. R. Soloperto, J. Köhler, und F. Allgöwer, „A Nonlinear MPC Scheme for Output Tracking Without Terminal Ingredients“, IEEE Transactions on Automatic Control, Bd. 68, Nr. 4, Art. Nr. 4, 2023, doi: 10.1109/TAC.2022.3173494.
    19. Y. Xie, J. Berberich, und F. Allgöwer, „Linear Data-Driven Economic MPC with Generalized Terminal Constraint“, IFAC World Congress, 2023.
    20. M. Köhler, M. A. Müller, und F. Allgöwer, „Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems“, IFAC-PapersOnLine, Bd. 56, Nr. 2, Art. Nr. 2, 2023, doi: 10.1016/j.ifacol.2023.10.1450.
    21. X. Wang, J. Berberich, J. Sun, G. Wang, F. Allgöwer, und J. Chen, „Model-based and data-driven control of event-and self-triggered discrete-time systems“, IEEE Trans. Cybernetics, Bd. 53, Nr. 9, Art. Nr. 9, 2023, doi: 10.1109/TCYB.2023.3272216.
    22. M. Alsalti, V. G. Lopez, J. Berberich, F. Allgöwer, und M. A. Müller, „Data-based control of feedback linearizable systems“, IEEE Trans. Automat. Control, Bd. 68, Nr. 11, Art. Nr. 11, 2023, doi: 10.1109/TAC.2023.3249289.
    23. R. Soloperto, M. A. Müller, und F. Allgöwer, „Guaranteed Closed-Loop Learning in Model Predictive Control“, IEEE Transactions on Automatic Control, Bd. 68, Nr. 2, Art. Nr. 2, 2023, doi: 10.1109/TAC.2022.3172453.
    24. T. Martin und 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.
    25. R. Strässer, M. Schaller, K. Worthmann, J. Berberich, und F. Allgöwer, „Koopman-based feedback design with stability guarantees“, submitted, Preprint: arxiv:2312.01441, 2023.
    26. J. Berberich, J. Köhler, M. A. Müller, und F. Allgöwer, „Linear tracking MPC for nonlinear systems part II: the data-driven case“, IEEE Trans. Automat. Control, Bd. 67, Nr. 9, Art. Nr. 9, 2022, doi: 10.1109/TAC.2022.3166851.
    27. P. Pauli, J. Berberich, und F. Allgöwer, „Robustness analysis and training of recurrent neural networks using dissipativity theory“, at - Automatisierungstechnik, Bd. 70, Nr. 8, Art. Nr. 8, 2022, doi: 10.1515/auto-2022-0032.
    28. C. Klöppelt, J. Berberich, F. Allgöwer, und 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.
    29. J. Berberich, J. Köhler, M. A. Müller, und F. Allgöwer, „Linear tracking MPC for nonlinear systems part I: the model-based case“, IEEE Trans. Automat. Control, Bd. 67, Nr. 9, Art. Nr. 9, 2022, doi: 10.1109/TAC.2022.3166872.
    30. S. Wildhagen, F. Dürr, und F. Allgöwer, „Rollout event-triggered control: reconciling event- and time-triggered control“, at - Automatisierungstechnik, Bd. 70, Nr. 4, Art. Nr. 4, 2022, doi: 10.1515/auto-2021-0111.
    31. M. Sharf, A. Koch, D. Zelazo, und F. Allgöwer, „Model-Free Practical Cooperative Control for Diffusively Coupled Systems“, IEEE Transactions on Automatic Control, Bd. 67, Nr. 2, Art. Nr. 2, 2022, doi: 10.1109/TAC.2021.3056582.
    32. M. Köhler, M. A. Müller, und F. Allgöwer, „Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems“, IEEE Trans. Automat. Control (submitted), Preprint:  arXiv:2210.10128, 2022.
    33. P. Pauli, A. Koch, J. Berberich, P. Kohler, und F. Allgöwer, „Training Robust Neural Networks using Lipschitz Bounds“, IEEE Control Systems Lett., Bd. 6, S. 121–126, 2021, doi: 10.1109/LCSYS.2021.3050444.
    34. M. Hertneck, S. Linsenmayer, und F. Allgöwer, „Efficient stability analysis approaches for nonlinear  weakly-hard real-time control systems“, Automatica, Bd. 133, S. 109868, 2021, doi: https://doi.org/10.1016/j.automatica.2021.109868.
    35. A. Koch, J. M. Montenbruck, und F. Allgöwer, „Sampling Strategies for Data-Driven Inference of Input-Output System Properties“, IEEE Trans. Automat. Control, Bd. 66, S. 1144–1159, 2021, doi: 10.1109/TAC.2020.2994894.
    36. T. Martin und F. Allgöwer, „Dissipativity verification with guarantees for polynomial systems from noisy input-state data“, IEEE Control Systems Lett., Bd. 5, Nr. 4, Art. Nr. 4, 2021, doi: 10.1109/LCSYS.2020.3037842.
    37. S. Linsenmayer, M. Hertneck, und F. Allgöwer, „Linear Weakly Hard Real-Time Control Systems: Time- and Event-Triggered Stabilization“, IEEE Trans.\ Automat.\ Control, Bd. 66, Nr. 4, Art. Nr. 4, 2021, doi: 10.1109/TAC.2020.3000981.
    38. J. Berberich, J. Köhler, M. A. Müller, und F. Allgöwer, „Data-driven model predictive control with stability and robustness guarantees“, IEEE Trans. Automat. Control, Bd. 66, Nr. 4, Art. Nr. 4, 2021, doi: 10.1109/TAC.2020.3000182.
    39. J. Berberich, J. Köhler, M. A. Müller, und F. Allgöwer, „Data-driven model predictive control: closed-loop guarantees and experimental results“, at-Automatisierungstechnik, Bd. 69, Nr. 7, Art. Nr. 7, 2021, doi: 10.1515/auto-2021-0024.
    40. S. Linsenmayer, B. W. Carabelli, S. Wildhagen, K. Rothermel, und F. Allgöwer, „Controller and Triggering Mechanism Co-Design for Control over Time-Slotted Networks“, IEEE Trans.\ Control of Network Systems, Bd. 8, Nr. 1, Art. Nr. 1, 2021, doi: 10.1109/TCNS.2020.3024316.
    41. A. Koch, J. Berberich, J. Köhler, und F. Allgöwer, „Determining optimal input–output properties: A data-driven approach“, Automatica, Bd. 134, S. 109906, 2021, doi: https://doi.org/10.1016/j.automatica.2021.109906.
    42. M. I. Müller, A. Koch, F. Allgöwer, und C. R. Rojas, „Data-Driven Input-Passivity Estimation Using Power Iterations“, IFAC-PapersOnLine, Bd. 54, Nr. 7, Art. Nr. 7, 2021, doi: https://doi.org/10.1016/j.ifacol.2021.08.429.
    43. P. Pauli, A. Koch, J. Berberich, P. Kohler, und F. Allgöwer, „Training Robust Neural Networks Using Lipschitz Bounds“, IEEE Control Systems Letters, Bd. 6, S. 121–126, 2021, doi: 10.1109/LCSYS.2021.3050444.
    44. S. Yu, M. Hirche, Y. Huang, H. Chen, und F. Allgöwer, „Model predictive control for autonomous ground vehicles: a review“, Auton. Intell. Syst., Bd. 1, S. 4, 2021, doi: 10.1007/s43684-021-00005-z.
    45. A. Koch, J. Berberich, und F. Allgöwer, „Provably robust verification of dissipativity properties from data“, IEEE Transactions on Automatic Control, Bd. 67, Nr. 8, Art. Nr. 8, 2021, doi: 10.1109/TAC.2021.3116179.
    46. P. Wenzelburger und F. Allgöwer, „Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0“, Applied Sciences, Bd. 11, Nr. 17, Art. Nr. 17, 2021, doi: 10.3390/app11178145.
    47. J. Köhler, L. Schwenkel, A. Koch, J. Berberich, P. Pauli, und F. Allgöwer, „Robust and optimal predictive control of the COVID-19 outbreak“, Annual reviews in Control, 2020.
    48. J. Köhler, M. A. Müller, und F. Allgöwer, „A nonlinear tracking model predictive control scheme for unreachable dynamic target signals“, Automatica, Bd. 118, S. 109030, 2020.
    49. J. Köhler, M. A. Müller, und F. Allgöwer, „Periodic optimal control of nonlinear constrained systems using economic model predictive control“, J. Proc. Contr., Bd. 92, S. 185–201, 2020.
    50. R. Soloperto, J. Köhler, und F. Allgöwer, „Augmenting MPC schemes with active learning: Intuitive tuning and guaranteed performance“, IEEE Control Systems Letters, Bd. 4, Nr. 3, Art. Nr. 3, 2020.
    51. J. Köhler, P. Kötting, R. Soloperto, F. Allgöwer, und M. A. Müller, „A robust adaptive model predictive control framework for nonlinear uncertain systems“, Int. J. Robust and Nonlinear Control, S. 1–25, 2020.
    52. J. Nubert, J. Köhler, V. Berenz, F. Allgöwer, und S. Trimpe, „Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control“, IEEE Robotics and Automation Letters, Bd. 5, Nr. 2, Art. Nr. 2, 2020.
    53. J. Köhler, M. A. Müller, und F. Allgöwer, „A nonlinear model predictive control framework using reference generic terminal ingredients“, IEEE Trans. Automat. Control, Bd. 65, Nr. 8, Art. Nr. 8, 2020.
    54. K. Kuritz, D. Stöhr, D. S. Maichl, N. Pollak, M. Rehm, und F. Allgöwer, „Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities“, Scientific Reports, Bd. 10, Nr. 1, Art. Nr. 1, 2020, doi: 10.1038/s41598-020-60400-z.
    55. J. Berberich, J. Köhler, F. Allgöwer, und M. A. Müller, „Dissipativity properties in constrained optimal control: A computational approach“, Automatica, Bd. 114, S. 108840, 2020, doi: 10.1016/j.automatica.2020.108840.
    56. J. Köhler, R. Soloperto, M. A. Müller, und F. Allgöwer, „A computationally efficient robust model predictive control framework for uncertain nonlinear systems“, IEEE Trans. Automat. Control, 2020.
    57. D. Imig, N. Pollak, F. Allgöwer, und M. Rehm, „Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis“, PLoS Computational Biology, Bd. 16, Nr. 6, Art. Nr. 6, 2020.
    58. S. Wildhagen, M. A. Müller, und F. Allgöwer, „Predictive Control over a Dynamical Token Bucket Network“, IEEE Control Systems Lett., Bd. 3, Nr. 4, Art. Nr. 4, 2019, doi: 10.1109/LCSYS.2019.2919264.
    59. F. D. Brunner, W. P. M. H. Heemels, und F. Allgöwer, „Event-triggered and self-triggered control for linear systems based on reachable sets“, Automatica, Bd. 101, S. 15–26, 2019.
    60. A. Romer, J. Berberich, J. Köhler, und F. Allgöwer, „One-shot verification of dissipativity properties from input-output data“, IEEE Control Systems Lett., Bd. 3, S. 709–714, 2019, doi: 10.1109/LCSYS.2019.2917162.
    61. J. Köhler, M. A. Müller, und F. Allgöwer, „Distributed model predictive control - Recursive feasibility under inexact dual optimization“, Automatica, Bd. 102, S. 1–9, 2019.
    62. F. Allgöwer u. a., „Position paper on the challenges posed by modern applications to cyber-physical systems theory“, Nonlinear Analysis: Hybrid Systems, Bd. 34, S. 147–165, 2019, doi: 10.1016/j.nahs.2019.05.007.
    63. S. Linsenmayer, D. V. Dimarogonas, und F. Allgöwer, „Periodic event-triggered control for networked control systems based on non-monotonic Lyapunov functions“, Automatica, Bd. 106, S. 35–46, 2019, doi: 10.1016/j.automatica.2019.04.039.
    64. S. Linsenmayer, D. V. Dimarogonas, und F. Allgöwer, „Event-Based Vehicle Coordination Using Nonlinear Unidirectional Controllers“, IEEE Trans. Control of Network Systems, Bd. 5, Nr. 4, Art. Nr. 4, 2018, doi: 10.1109/TCNS.2017.2733959.
    65. F. A. Lincoln u. a., „Sensitization of glioblastoma cells to TRAIL- induced apoptosis by IAP- and Bcl-2 antagonism“, Cell Death and Disease, Bd. 9, Nr. 1112, Art. Nr. 1112, 2018, doi: 10.1038/s41419-018-1160-2.
    66. L. Danish, D. Imig, F. Allgöwer, P. Scheurich, und 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, Bd. 13, Nr. 6, Art. Nr. 6, 2018, doi: https://doi.org/10.1371/journal.pone.0198203.
    67. M. Hertneck, J. Köhler, S. Trimpe, und F. Allgöwer, „Learning an approximate model predictive controller with guarantees“, IEEE Control Systems Lett., Bd. 2, Nr. 3, Art. Nr. 3, 2018, doi: 10.1109/LCSYS.2018.2843682.
    68. D. Imig, K. Kuritz, N. Pollak, M. Rehm, und F. Allgöwer, „Death patterns resulting from cell cycle-independent cell death“, IFAC-PapersOnLine, Bd. 51, Nr. 19, Art. Nr. 19, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.09.028.
    69. F. D. Brunner, M. A. Müller, und F. Allgöwer, „Enhancing Output-feedback MPC with Set-valued Moving Horizon Estimation“, IEEE Transactions on Automatic Control, Bd. 63, Nr. 9, Art. Nr. 9, 2018.
    70. S. Linsenmayer, H. Ishii, und F. Allgöwer, „Containability with event-based sampling for scalar systems with time-varying delay and uncertainty“, IEEE Control Systems Lett., Bd. 2, Nr. 4, Art. Nr. 4, 2018, doi: 10.1109/lcsys.2018.2847449.
    71. F. A. Bayer, M. A. Müller, und F. Allgöwer, „On optimal system operation in robust economic MPC“, Automatica, Bd. 88, S. 98–106, 2018, doi: https://doi.org/10.1016/j.automatica.2017.11.007.
    72. J. Köhler, M. A. Müller, und F. Allgöwer, „On periodic dissipativity notions in economic model predictive control“, IEEE Control Systems Letters, Bd. 2, Nr. 3, Art. Nr. 3, 2018.
    73. P. N. Köhler, M. A. Müller, und F. Allgöwer, „A distributed economic MPC framework for cooperative control under conflicting objectives“, Automatica, Bd. 96, S. 368–379, 2018, doi: https://doi.org/10.1016/j.automatica.2018.07.001.
    74. J. Berberich, J. Köhler, F. Allgöwer, und M. A. Müller, „Indefinite Linear Quadratic Optimal Control: Strict Dissipativity and Turnpike Properties“, IEEE Control Systems Lett., Bd. 2, Nr. 3, Art. Nr. 3, 2018, doi: 10.1109/LCSYS.2018.2842142.
    75. J. Köhler, M. A. Müller, und F. Allgöwer, „Nonlinear reference tracking: An economic model predictive control perspective“, IEEE Trans. Automat. Control, Bd. 64, S. 254–269, 2018.
    76. K. Kuritz, D. Imig, M. Dyck, und F. Allgöwer, „Ensemble control for cell cycle synchronization of heterogeneous cell populations“, IFAC-PapersOnLine, Bd. 51, Nr. 19, Art. Nr. 19, 2018, doi: https://doi.org/10.1016/j.ifacol.2018.09.034.
    77. K. Kuritz, S. Zeng, und F. Allgöwer, „Ensemble Controllability of Cellular Oscillators“, IEEE Control Systems Letters, Bd. 3, Nr. 2, Art. Nr. 2, 2018, doi: 10.1109/LCSYS.2018.2870967.
    78. F. D. Brunner, D. Antunes, und F. Allgöwer, „Stochastic thresholds in event-triggered control: A consistent policy for quadratic control“, Automatica, Bd. 89, S. 376–381, 2018.
    79. M. Lorenzen, F. Dabbene, R. Tempo, und F. Allgöwer, „Stochastic MPC with Offline Uncertainty Sampling“, Automatica, Bd. 81, S. 176–183, 2017, doi: https://doi.org/10.1016/j.automatica.2017.03.031.
    80. M. A. Müller und F. Allgöwer, „Economic and distributed model predictive control: recent developments in optimization-based control“, SICE Journal of Control, Measurement, and System Integration, Bd. 10, Nr. 2, Art. Nr. 2, 2017.
    81. G. Goebel und F. Allgöwer, „New results on semi-explicit and almost explicit MPC algorithms“, at-Automatisierungstechnik, Bd. 65, Nr. 4, Art. Nr. 4, 2017.
    82. G. Goebel und F. Allgöwer, „Semi-explicit MPC based on subspace clustering“, Automatica, Bd. 83, S. 309–316, 2017.
    83. W. Halter, J. M. Montenbruck, Z. A. Tuza, und F. Allgöwer, „A resource dependent protein synthesis model for evaluating synthetic circuits“, J. Theor. Biol., Bd. 420, S. 267–278, 2017.
    84. K. Kuritz, D. Stöhr, N. Pollak, und F. Allgöwer, „On the relationship between cell cycle analysis with ergodic principles and age-structured cell population models“, J. Theor. Biol., Bd. 414, S. 91–102, 2017, doi: 10.1016/j.jtbi.2016.11.024.
    85. M. Lorenzen, F. Dabbene, R. Tempo, und F. Allgöwer, „Constraint-Tightening and Stability in Stochastic Model Predictive Control“, IEEE Trans. Automat. Control, Bd. 62, Nr. 7, Art. Nr. 7, 2017, doi: 10.1109/TAC.2016.2625048.
    86. C. Thomaseth, K. Kuritz, F. Allgoewer, und R. N., „The circuit-breaking algorithm for monotone systems“, Mathematical Biosciences, Bd. 284, S. 80–91, 2017.
    87. M. Lorenzen, M. A. Müller, und F. Allgöwer, „Stochastic Model Predictive Control without Terminal Constraints“, Int. J. Robust and Nonlinear Control, 2017, doi: 10.1002/rnc.3912.
    88. S. Zeng und F. Allgöwer, „Structured optimal feedback in multi-agent systems: A static output feedback perspective“, Automatica, Bd. 76, S. 214–221, 2017, doi: 10.1016/j.automatica.2016.10.021.
    89. Y. Liu u. a., „Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances“, Robotics and Autonomous Systems, Bd. 98, S. 317–332, 2017.
    90. J. M. Montenbruck, M. Arcak, und F. Allgöwer, „An Input-Output Framework for Submanifold Stabilization“, IEEE Trans. Automat. Control, Bd. 62, Nr. 10, Art. Nr. 10, 2017.
    91. J. M. Montenbruck, D. Zelazo, und F. Allgöwer, „Fekete Points, Formation Control, and the Balancing Problem“, IEEE Trans. Automat. Control, Bd. 62, Nr. 10, Art. Nr. 10, 2017.
  2. Konferenzen

    1. Yifan Xie, Julian Berberich, Frank Allgöwer, „Data-Driven Min-Max MPC for Linear Systems“, 2023.
  3. Beiträge in Sammelband

    1. K. Kuritz, W. Halter, und 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, und P. Misra, Hrsg., in Emerging Applications of Control and Systems Theory: A Festschrift in Honor of Mathukumalli Vidyasagar. , Cham: Springer International Publishing, 2018, S. 1–13. doi: 10.1007/978-3-319-67068-3_1.
  4. Konferenzbeiträge

    1. R. Strässer, M. Schaller, K. Worthmann, J. Berberich, und F. Allgöwer, „SafEDMD: A certified learning architecture tailored to data-driven control of nonlinear dynamical systems“, in submitted, Preprint: arxiv:2402.03145, in submitted, Preprint: arxiv:2402.03145. 2024.
    2. R. Strässer u. a., „Collision Avoidance Safety Filter for an Autonomous E-Scooter using Ultrasonic Sensors“, in submitted, Preprint: arxiv:2403.15116, in submitted, Preprint: arxiv:2403.15116. 2024.
    3. M. Hertneck und F. Allgöwer, „Reverse average dwell time constraints enable arbitrary maximum allowable transmission intervals“, in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS), in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS). Canberra, Australia, 2023, S. 379–384. doi: 10.1016/j.ifacol.2023.02.064.
    4. J. Berberich, A. Iannelli, A. Padoan, J. Coulson, F. Dörfler, und F. Allgöwer, „A quantitative and constructive proof of Willems’ Fundamental Lemma and its implications“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). San Diego, CA, USA, 2023, S. 4155–4160. doi: 10.23919/ACC55779.2023.10156227.
    5. H. Schlüter und F. Allgöwer, „Stochastic Model Predictive Control using Initial State and Variance Interpolation“, in Proc. 62nd IEEE Conference on Decision and Control (CDC), in Proc. 62nd IEEE Conference on Decision and Control (CDC). Singapore: IEEE, Dez. 2023. doi: 10.1109/CDC49753.2023.10383711.
    6. T. Martin, T. B. Schön, und F. Allgöwer, „Gaussian inference for data-driven state-feedback design of nonlinear systems“, in 22nd IFAC World Congress, in 22nd IFAC World Congress. 2023, S. 4796–4803. doi: doi.org/10.1016/j.ifacol.2023.10.1245.
    7. R. Strässer, J. Berberich, und F. Allgöwer, „Robust data-driven control for nonlinear systems using the Koopman operator“, in Proc. 22nd IFAC World Congress, in Proc. 22nd IFAC World Congress, vol. 56. 2023, S. 2257–2262. doi: https://doi.org/10.1016/j.ifacol.2023.10.1190.
    8. S. Schlor, R. Strässer, und F. Allgöwer, „Koopman interpretation and analysis of a public-key cryptosystem: Diffie-Hellman key exchange“, in Proc. 22nd IFAC World Congress, in Proc. 22nd IFAC World Congress. Yokohama, Japan, 2023, S. 984–990. doi: 10.1016/j.ifacol.2023.10.1693.
    9. M. Hertneck und F. Allgöwer, „Self-triggered output feedback control for nonlinear networked control systems based on hybrid Lyapunov functions“, in Proc. 22nd IFAC World Congress, in Proc. 22nd IFAC World Congress. Tokyo, Japan, 2023, S. 5748–5753. doi: 10.1016/j.ifacol.2023.10.165.
    10. P. Pauli, D. Gramlich, und F. Allgöwer, „Lipschitz constant estimation for 1D convolutional neural networks“, in Proc. 5th Annual Learning for Dynamics and Control Conf. (L4DC), in Proc. 5th Annual Learning for Dynamics and Control Conf. (L4DC), vol. 211. Philadelphia, PA, USA: PMLR, 2023, S. 1321--1332.
    11. M. Alsalti, V. G. Lopez, J. Berberich, F. Allgöwer, und M. A. Müller, „Data-driven nonlinear predictive control for feedback linearizable systems“, in Proc. 22nd IFAC World Congress, in Proc. 22nd IFAC World Congress. Yokohama, Japan, 2023, S. 617–624. doi: 10.1016/j.ifacol.2023.10.1636.
    12. R. Strässer, J. Berberich, und F. Allgöwer, „Control of bilinear systems using gain-scheduling: Stability and performance guarantees“, in 62nd IEEE Conference on Decision and Control (CDC), in 62nd IEEE Conference on Decision and Control (CDC). Singapore, Singapore, 2023, S. 4674–4681. doi: 10.1109/CDC49753.2023.10384021.
    13. D. Meister, F. Dürr, und F. Allgöwer, „Shared Network Effects in Time- versus Event-Triggered Consensus of a Single-Integrator Multi-Agent System“, in 22nd IFAC World Congress, in 22nd IFAC World Congress. Yokohama, Japan, 2023, S. 5975–5980. doi: 10.1016/j.ifacol.2023.10.636.
    14. Z. Ma, H. Schlüter, F. Berkel, T. Specker, und F. Allgöwer, „Recursive Feasibility and Stability for Stochastic MPC based on Polynomial Chaos“, in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS), in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS), vol. 56. Canberra, Australia: Elsevier, Jan. 2023, S. 204–209. doi: 10.1016/j.ifacol.2023.02.035.
    15. D. Meister und F. Allgöwer, „Performance implications of different p-norms in level-triggered sampling“, in Proc. 62nd IEEE Conf. on Decision and Control (CDC), in Proc. 62nd IEEE Conf. on Decision and Control (CDC). Singapore, Singapore, 2023, S. 3878–3883. doi: 10.1109/CDC49753.2023.10384009.
    16. D. Antunes, D. Meister, T. Namerikawa, F. Allgöwer, und W. P. M. H. Heemels, „Consistent event-triggered consensus on complete graphs“, in Proc. 62nd IEEE Conf. on Decision and Control (CDC), in Proc. 62nd IEEE Conf. on Decision and Control (CDC). Singapore, Singapore, 2023, S. 3911–3916. doi: 10.1109/CDC49753.2023.10384026.
    17. L. Schwenkel, J. Köhler, M. A. Müller, und F. Allgöwer, „Robust peak-to-peak gain analysis using integral quadratic constraints“, in Proc. 22nd IFAC World Congress, in Proc. 22nd IFAC World Congress. Yokohama, Japan, 2023, S. 11564–11569. doi: 10.1016/j.ifacol.2023.10.452.
    18. M. Hertneck und F. Allgöwer, „Dynamic self-triggered control for nonlinear systems with delays“, in Proc. 9th IFAC Conf. on Networked Systems (NECSYS), in Proc. 9th IFAC Conf. on Networked Systems (NECSYS). Zürich, Switzerland, 2022, S. 312–317. doi: 10.1016/j.ifacol.2022.07.278.
    19. T. Martin und F. Allgöwer, „Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). Atlanta, GA, USA, 2022, S. 1432–1437. doi: 10.23919/ACC53348.2022.9867806.
    20. H. Schlüter und F. Allgöwer, „Stochastic model predictive control using initial state optimization“, in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems (MTNS), in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems (MTNS), vol. 55. Bayreuth, Germany: Elsevier, Nov. 2022, S. 454–459. doi: 10.1016/j.ifacol.2022.11.095.
    21. M. Köhler, J. Berberich, M. A. Müller, und F. Allgöwer, „Data-driven distributed MPC of dynamically coupled linear systems“, in Proc. 25th Int. Symp. Math. Theory Netw. Syst. (MTNS), in Proc. 25th Int. Symp. Math. Theory Netw. Syst. (MTNS). Bayreuth, Germany, 2022, S. 365–370. doi: 10.1016/j.ifacol.2022.11.080.
    22. D. Meister, F. Aurzada, M. A. Lifshits, und 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), in Proc. 61st IEEE Conf. on Decision and Control (CDC). Cancun, Mexico, 2022, S. 441–446. doi: 10.1109/CDC51059.2022.9993301.
    23. P. Pauli, N. Funcke, D. Gramlich, M. A. Msalmi, und F. Allgöwer, „Neural network training under semidefinite constraints“, in 2022 IEEE 61st Conference on Decision and Control (CDC), in 2022 IEEE 61st Conference on Decision and Control (CDC). Dez. 2022, S. 2731–2736. doi: 10.1109/CDC51059.2022.9992331.
    24. S. Wildhagen, M. Pezzutto, L. Schenato, und 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), in 2022 IEEE 61st Conference on Decision and Control (CDC). 2022, S. 7670–7675. doi: 10.1109/CDC51059.2022.9992581.
    25. R. Drummond, S. Duncan, M. Turner, P. Pauli, und F. Allgower, „Bounding the difference between model predictive control and neural networks“, in Learning for Dynamics and Control Conference, in Learning for Dynamics and Control Conference. PMLR, 2022, S. 817--829.
    26. D. Müller, J. Feilhauer, J. Wickert, J. Berberich, F. Allgöwer, und O. Sawodny, „Data-driven predictive disturbance observer for quasi continuum manipulators“, in Proc. 61st IEEE Conf. Decision and Control (CDC), in Proc. 61st IEEE Conf. Decision and Control (CDC). Cancun, Mexico, 2022, S. 1816–1822. doi: 10.1109/CDC51059.2022.9992740.
    27. J. Berberich, J. Köhler, M. A. Müller, und F. Allgöwer, „Stability in data-driven MPC: an inherent robustness perspective“, in Proc. 61st IEEE Conf. Decision and Control (CDC), in Proc. 61st IEEE Conf. Decision and Control (CDC). Cancun, Mexico, 2022, S. 1105–1110. doi: 10.1109/CDC51059.2022.9993361.
    28. P. Pauli, D. Gramlich, J. Berberich, und 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), in Proc. 60th IEEE Conf. on Decision and Control (CDC). Austin, TX, USA, 2021, S. 3611–3618.
    29. P. Pauli, J. Köhler, J. Berberich, A. Koch, und F. Allgöwer, „Offset-free setpoint tracking using neural network controllers“, in Proc. 3rd Conf. on Learning for Dynamics and Control (L4DC), in Proc. 3rd Conf. on Learning for Dynamics and Control (L4DC). Zurich, Switzerland, 2021, S. 992–1003.
    30. A. Alanwar, A. Koch, F. Allgöwer, und 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 the 3rd Conference on Learning for Dynamics and Control, vol. 144. 2021, S. 163--175.
    31. M. Hertneck und 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), in Proc. 3rd IFAC Conf. on Modelling, Identification and Control of Nonlinear Systems (MICNON). Tokyo, Japan, 2021, S. 443–448. doi: 10.1016/j.ifacol.2021.10.390.
    32. N. Wieler, J. Berberich, A. Koch, und 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 the 3rd Conference on Learning for Dynamics and Control, vol. 144. PMLR, 2021, S. 287--298.
    33. R. Soloperto, P. Wenzelburger, D. Meister, D. Scheuble, V. S. M. Breidohr, und F. Allgöwer, „A control framework for autonomous e-scooters“, in Proc. 16th IFAC Symposium on Control in Transportation Systems (CTS), in Proc. 16th IFAC Symposium on Control in Transportation Systems (CTS). Lille, France, 2021, S. 252–258. doi: 10.1016/j.ifacol.2021.06.030.
    34. J. Berberich, S. Wildhagen, M. Hertneck, und F. Allgöwer, „Data-driven analysis and control of continuous-time systems under aperiodic sampling“, in Proc. 19th IFAC Symp. System Identification (SYSID), in Proc. 19th IFAC Symp. System Identification (SYSID). Padova, Italy, 2021, S. 210–215. doi: 10.1016/j.ifacol.2021.08.360.
    35. C. Klöppelt, L. Schwenkel, F. Allgöwer, und M. A. Müller, „Transient Performance of Tube-based Robust Economic Model Predictive Control“, in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC). Bratislava, Slovakia, 2021, S. 28–35. doi: 10.1016/j.ifacol.2021.08.520.
    36. J. Berberich, J. Köhler, M. A. Müller, und F. Allgöwer, „On the design of terminal ingredients for data-driven MPC“, in Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC), in Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC). Bratislava, Slovakia, 2021, S. 257–263. doi: 10.1016/j.ifacol.2021.08.554.
    37. M. Alsalti, J. Berberich, V. G. Lopez, F. Allgöwer, und M. A. Müller, „Data-Based System Analysis and Control of Flat Nonlinear Systems“, in Proc. 60th IEEE Conf. Decision and Control (CDC), in Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, S. 1484–1489. doi: 10.1109/CDC45484.2021.9683327.
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    39. S. Schlor, M. Hertneck, S. Wildhagen, und F. Allgöwer, „Multi-party computation enables secure polynomial control based solely on secret-sharing“, in Proc. 60th IEEE Conf. Decision and Control (CDC), in Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, S. 4882–4887. doi: 10.1109/CDC45484.2021.9683026.
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    41. J. Venkatasubramanian, J. Köhler, J. Berberich, und F. Allgöwer, „Robust dual control based on gain scheduling“, in 2020 59th IEEE Conference on Decision and Control (CDC), in 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2021, S. 2270–2277. doi: 10.1109/CDC42340.2020.9304336.
    42. R. Strässer, J. Berberich, und F. Allgöwer, „Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics“, in Proc. 60th IEEE Conf. Decision and Control (CDC), in Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, S. 4344–4351. doi: 10.1109/CDC45484.2021.9683211.
    43. E. Müller, P. N. Köhler, K. Y. Pettersen, und F. Allgöwer, „Economic model predictive control for obstacle-aided snake robot locomotion“, in Proc. 21st IFAC World Congress, in Proc. 21st IFAC World Congress. Berlin, Germany, 2020.
    44. P. Pauli, A. Koch, und F. Allgöwer, „Smartphone Apps for Learning Progress and Course Revision“, in Proc.\ 21st IFAC World Congress, in Proc.\ 21st IFAC World Congress. Berlin, Germany, Juli 2020.
    45. D. Persson, A. Koch, und F. Allgöwer, „Probabilistic H2-norm estimation via Gaussian process system identification“, in Proc. 21st IFAC World Congress, in Proc. 21st IFAC World Congress. Berlin, Germany, 2020, S. 431–436. doi: 10.1016/j.ifacol.2020.12.211.
    46. J. Berberich, A. Koch, C. W. Scherer, und F. Allgöwer, „Robust data-driven state-feedback design“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). Denver, CO, USA, 2020, S. 1532–1538. doi: 10.23919/ACC45564.2020.9147320.
    47. S. Wildhagen und F. Allgöwer, „Scheduling and control over networks using MPC with time-varying terminal ingredients“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). Denver, CO, USA, 2020, S. 1913–1918. doi: 10.23919/ACC45564.2020.9147411.
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    52. M. Hertneck, S. Linsenmayer, und F. Allgöwer, „Model-Based Nonlinear Periodic Event-Triggered Control for Continuous-Time Systems with Sampled-Data Prediction“, in Proc. European Control Conf. (ECC), in Proc. European Control Conf. (ECC). Saint Petersburg, Russia, 2020, S. 1814–1819. doi: 10.1109/CDC40024.2019.9029770.
    53. M. Hirche, P. N. Köhler, M. A. Müller, und F. Allgöwer, „Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems“, in Proc. 59th IEEE Conf. on Decision and Control (CDC), in Proc. 59th IEEE Conf. on Decision and Control (CDC). Jeju Island, Republic of Korea, 2020, S. 1248–1253. doi: 10.1109/CDC42340.2020.9303838.
    54. A. Koch, J. Berberich, und F. Allgöwer, „Verifying dissipativity properties from noise-corrupted input-state data“, in Proc. 59th IEEE Conf. on Decision and Control (CDC), in Proc. 59th IEEE Conf. on Decision and Control (CDC). Jeju, South Korea, 2020, S. 616–621. doi: 10.1109/CDC42340.2020.9304380.
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    56. A. Camisa, P. N. Köhler, M. A. Müller, G. Notarstefano, und F. Allgöwer, „A distributed optimization algorithm for Nash bargaining in multi-agent systems“, in Proc. 21st IFAC World Congress, in Proc. 21st IFAC World Congress. Berlin, Germany, 2020.
    57. L. Schwenkel, J. Köhler, M. A. Müller, und F. Allgöwer, „Robust Economic Model Predictive Control without Terminal Conditions“, in Proc. of 21st IFAC World Congress, in Proc. of 21st IFAC World Congress. 2020. doi: 10.1016/j.ifacol.2020.12.465.
    58. J. Köhler, M. A. Müller, und F. Allgöwer, „Implicit solutions to constrained nonlinear output regulation using MPC“, in Proc.\ 59th IEEE Conf.\ Decision and Control (CDC), in Proc.\ 59th IEEE Conf.\ Decision and Control (CDC). 2020, S. 4604–4609.
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    60. A. Koch, M. Lorenzen, P. Pauli, und F. Allgöwer, „Facilitating learning progress in a first control course via Matlab apps“, in Proc. 21st IFAC World Congress, in Proc. 21st IFAC World Congress. Berlin, Germany, 2020, S. 17356–17361. doi: 10.1016/j.ifacol.2020.12.2086.
    61. T. Martin, A. Koch, und F. Allgöwer, „Data-driven surrogate models for LTI systems via saddle-point dynamics“, in Proc. 21st IFAC World Congress, in Proc. 21st IFAC World Congress. Berlin, Germany, 2020, S. 971–976. doi: 10.1016/j.ifacol.2020.12.1261.
    62. J. Berberich und F. Allgöwer, „A trajectory-based framework for data-driven system analysis and control“, in Proc. European Control Conf. (ECC), in Proc. European Control Conf. (ECC). Saint Petersburg, Russia, 2020, S. 1365–1370. doi: 10.23919/ECC51009.2020.9143608.
    63. M. Rosenfelder, J. Köhler, und F. Allgöwer, „Stability and performance in transient average constrained economic MPC without terminal constraints“, in Proc.\ 21st IFAC World Congress, in Proc.\ 21st IFAC World Congress. 2020.
    64. F. Jaumann, S. Wildhagen, und F. Allgöwer, „Saving Tokens in Rollout Control with Token Bucket Specification“, in Proc. 21st IFAC World Congress, in Proc. 21st IFAC World Congress. Berlin, Germany, 2020, S. 2662–2669. doi: 10.1016/j.ifacol.2020.12.313.
    65. T. Martin und F. Allgöwer, „Iterative data-driven inference of nonlinearity measures via successive graph approximation“, in Proc. 59th IEEE Conf. Decision and Control (CDC), in Proc. 59th IEEE Conf. Decision and Control (CDC). Jeju, South Korea, 2020, S. 4760–4765. doi: 10.1109/CDC42340.2020.9304285.
    66. Y. Lian, S. Wildhagen, Y. Jiang, B. Houska, F. Allgöwer, und C. N. Jones, „Resource-Aware Asynchronous Multi-Agent Coordination Via Self-Triggered MPC“, in 59th IEEE Conf. Decision and Control (CDC), in 59th IEEE Conf. Decision and Control (CDC). Jeju, South Korea, 2020, S. 685–690. doi: 10.1109/CDC42340.2020.9304137.
    67. P. Wenzelburger und F. Allgöwer, „A first step towards an autonomously driving E-Scooter“, in Demonstrator Session 21st IFAC World Congress, in Demonstrator Session 21st IFAC World Congress. Berlin, Germany, 2020. [Online]. Verfügbar unter: https://www.ist.uni-stuttgart.de/institute/team/pdf/PW/IFAC20_E-Scooter.pdf
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    71. H. Schlüter und F. Allgöwer, „A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances“, in Proc.\ 21th IFAC World Congress, in Proc.\ 21th IFAC World Congress. Berlin, Germany, Juli 2020, S. 7130–7135. doi: 10.1016/j.ifacol.2020.12.518.
    72. T. Martin, P. N. Köhler, und F. Allgöwer, „Dissipativity and Economic Model Predictive Control for Optimal Set Operation“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). Philadelphia, PA, USA, 2019, S. 1020–1026. doi: 10.23919/ACC.2019.8814305.
    73. R. Soloperto, J. Köhler, M. A. Müller, und F. Allgöwer, „Dual Adaptive MPC for output tracking of linear systems“, in Proc. 58th Conference on Decision and Control (CDC), in Proc. 58th Conference on Decision and Control (CDC). Nice, France, 2019.
    74. J. Köhler, M. A. Müller, und F. Allgöwer, „A simple framework for nonlinear robust output-feedback MPC“, in Proc. 18th European Control Conference (ECC), in Proc. 18th European Control Conference (ECC). Naples, Italy, 2019, S. 793–798.
    75. J. Köhler, E. Andina, R. Soloperto, M. A. Müller, und F. Allgöwer, „Linear robust adaptive model predictive control: Computational complexity and conservatism“, in Proc. 58th IEEE Conference on Decision and Control (CDC), in Proc. 58th IEEE Conference on Decision and Control (CDC). Nice, France, 2019, S. 1383–1388.
    76. S. Wildhagen, M. A. Müller, und F. Allgöwer, „Economic MPC using a Cyclic Horizon with Application to Networked Control Systems“, in Proc. 11th IFAC Symp. Nonlinear Control Systems (NOLCOS), in Proc. 11th IFAC Symp. Nonlinear Control Systems (NOLCOS). Vienna, Austria, 2019, S. 796–801. doi: 10.1016/j.ifacol.2019.12.011.
    77. P. Wenzelburger und 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), in Proc. 9th IFAC Conf. Manufacturing Modeling, Management, and Control (MIM). Berlin, Germany, 2019, S. 492–498. doi: 10.1016/j.ifacol.2019.11.111.
    78. T. Martin und F. Allgöwer, „Nonlinearity Measures for Data-Driven System Analysis and Control“, in Proc. 58th IEEE Conf. Decision and Control (CDC), in Proc. 58th IEEE Conf. Decision and Control (CDC). Nice, France, 2019, S. 3605–3610. doi: 10.1109/CDC40024.2019.9029804.
    79. P. Wenzelburger und F. Allgöwer, „A Novel Optimal Online Scheduling Scheme for Flexible Manufacturing Systems“, in Proc. 13th IFAC Workshop on Intelligent Manufacturing Systems (IMS), in Proc. 13th IFAC Workshop on Intelligent Manufacturing Systems (IMS). Oshawa, Canada, 2019, S. 1–6. doi: 10.1016/j.ifacol.2019.10.002.
    80. M. Hertneck, S. Linsenmayer, und 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), in Proc. 58th IEEE Conf. Decision and Control (CDC). Nice, France, 2019, S. 1680–1685. doi: 10.1109/CDC40024.2019.9029770.
    81. S. Linsenmayer, M. A. Müller, H. Ishii, und 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), in Proc. 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys). Chicago, IL, USA, 2019, S. 31–36. doi: 10.1016/j.ifacol.2019.12.138.
    82. J. Berberich, M. Sznaier, und F. Allgöwer, „Signal estimation and system identification with nonlinear dynamic sensors“, in 3rd IEEE Conf. Control Technology and Applications (CCTA), in 3rd IEEE Conf. Control Technology and Applications (CCTA). Hong Kong, China, 2019, S. 505–510. doi: 10.1109/CCTA.2019.8920592.
    83. P. N. Köhler, M. A. Müller, und F. Allgöwer, „Graph topology and subsystem centrality in approximately dissipative system interconnections“, in Proc. 58th IEEE Conference on Decision and Control (CDC), in Proc. 58th IEEE Conference on Decision and Control (CDC). Nice, France, 2019, S. 7441–7447.
    84. A. Romer, S. Trimpe, und F. Allgöwer, „Data-driven inference of passivity properties via Gaussian process optimization“, in Proc. European Control Conf. (ECC), in Proc. European Control Conf. (ECC). Naples, Italy, 2019, S. 29–35. doi: 10.23919/ECC.2019.8795728.
    85. P. N. Köhler, M. A. Müller, und F. Allgöwer, „Approximate dissipativity and performance bounds for interconnected systems“, in Proc. 18th European Control Conference (ECC), in Proc. 18th European Control Conference (ECC). Naples, Italy, 2019, S. 787–792.
    86. S. Linsenmayer, B. W. Carbelli, F. Dürr, J. Falk, F. Allgöwer, und 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), in Proc. 16th IEEE Annual Consumer Communications Networking Conf. (CCNC). Las Vegas, NV, USA, 2019, S. 1–6. doi: 10.1109/CCNC.2019.8651811.
    87. W. Halter, S. Michalowsky, und F. Allgöwer, „Extremum seeking for optimal enzyme production under cellular fitness constraints“, in Proc. European Control Conf. (ECC), in Proc. European Control Conf. (ECC). Neapel, Italien, 2019.
    88. R. Soloperto, J. Köhler, M. A. Müller, und F. Allgöwer, „Collision avoidance for uncertain nonlinear systems and moving obstacles using robust Model Predictive Control“, in Proc. 18th European Control Conference (ECC), in Proc. 18th European Control Conference (ECC). Naples, Italy, 2019.
    89. M. Nonhoff, P. N. Köhler, und F. Allgöwer, „Economic model predictive control for snake robot locomotion“, in Proc. 58th IEEE Conference on Decision and Control (CDC), in Proc. 58th IEEE Conference on Decision and Control (CDC). Nice, France, 2019.
    90. A. Romer, J. M. Montenbruck, und F. Allgöwer, „Some ideas on sampling strategies for data-driven inference of passivity properties for MIMO systems“, in Proc. American Control Conference (ACC), in Proc. American Control Conference (ACC). Milwaukee, Wisconsin, USA, 2018, S. 6094–6100. doi: 10.23919/ACC.2018.8431399.
    91. A. Romer, J. M. Montenbruck, und F. Allgöwer, „Data-driven inference of conic relations via saddle-point dynamics“, in Proc. 9th IFAC Symp. Robust Control Design (ROCOND), in Proc. 9th IFAC Symp. Robust Control Design (ROCOND). Florianópolis, Brazil, 2018, S. 586–591. doi: 10.1016/j.ifacol.2018.11.139.
    92. W. Halter, F. Allgöwer, R. M. Murray, und A. Gyorgy, „Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts“, in Proc. 57th IEEE Conf. Decision and Control (CDC), in Proc. 57th IEEE Conf. Decision and Control (CDC). Miami Beach, USA, 2018.
    93. S. Linsenmayer und F. Allgöwer, „Performance oriented triggering mechanisms with guaranteed traffic characterization for linear discrete-time systems“, in Proc. European Control Conf. (ECC), in Proc. European Control Conf. (ECC). Limassol, Cyprus, 2018, S. 1474–1479. doi: 10.23919/ECC.2018.8550568.
    94. J. Köhler, M. A. Müller, und F. Allgöwer, „MPC for nonlinear periodic tracking using reference generic offine computations“, in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), in Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC). Madison, Wisconsin, 2018, S. 656–661.
    95. J. Köhler, M. A. Müller, und F. Allgöwer, „Nonlinear Reference Tracking with Model Predictive Control: An Intuitive Approach“, in Proc. European Control Conf. (ECC), in Proc. European Control Conf. (ECC). 2018, S. 1355–1360.
    96. J. Köhler, M. A. Müller, und F. Allgöwer, „A novel constraint tightening approach for nonlinear robust model predictive control“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). 2018, S. 728–734.
    97. J. Köhler, C. Enyioha, und F. Allgöwer, „Dynamic Resource Allocation to Control Epidemic Outbreaks -A Model Predictive Control Approach“, in Proc. American Control Conf.(ACC), in Proc. American Control Conf.(ACC). Milwaukee, Wisconsin, 2018, S. 1546–1551.
    98. R. Soloperto, M. A. Müller, und F. Allgöwer, „Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty“, in Proc. 6th IFAC Conference on Nonlinear Model Predictive Control, in Proc. 6th IFAC Conference on Nonlinear Model Predictive Control. Madison, Wisconsin, 2018.
    99. P. N. Köhler, M. A. Müller, und F. Allgöwer, „Interconnections of dissipative systems and distributed economic MPC“, in Proc. 6th IFAC Conference on Nonlinear Model Predictive Control, in Proc. 6th IFAC Conference on Nonlinear Model Predictive Control. Madison, Wisconsin, 2018, S. 88–93.
    100. J. M. Montenbruck und F. Allgöwer, „Separable matrices and minimum complexity controllers“, in Proc. 56th IEEE Conf. Decision and Control (CDC), in Proc. 56th IEEE Conf. Decision and Control (CDC). 2017, S. 4187–4192.
    101. A. Romer, J. M. Montenbruck, und F. Allgöwer, „Sampling strategies for data-driven inference of passivity properties“, in Proc. 56th IEEE Conf. Decision and Control (CDC), in Proc. 56th IEEE Conf. Decision and Control (CDC). Melbourne, Victoria, Australia, 2017, S. 6389–6394. doi: 10.1109/CDC.2017.8264623.
    102. S. Zeng, J. M. Montenbruck, und F. Allgöwer, „Periodic Signal Compressors“, in Proc. 20th World Congress of the International Federation of Automatic Control, in Proc. 20th World Congress of the International Federation of Automatic Control. 2017, S. 6649–6654.
    103. M. Lorenzen, F. Allgöwer, und M. Cannon, „Adaptive Model Predictive Control with Robust Constraint Satisfaction“, in Proc. 20th IFAC World Congress, in Proc. 20th IFAC World Congress. Toulouse, France, 2017, S. 3368–3373.
    104. J. Köhler, M. A. Müller, N. Li, und 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), in Proc. 56th IEEE Conf. Decision and Control (CDC). Melbourne, Victoria, Australia, 2017, S. 6340–6345.
    105. W. Halter, Z. A. Tuza, und F. Allgöwer, „Signal differentiation with genetic networks“, in Proc. 20th IFAC World Congress, in Proc. 20th IFAC World Congress. Toulouse, France, 2017.
    106. S. Linsenmayer, R. Blind, und F. Allgöwer, „Delay-dependent data rate bounds for containability of scalar systems“, in Proc. 20th IFAC World Congress, in Proc. 20th IFAC World Congress. Toulouse, France, 2017, S. 7875–7880. doi: 10.1016/j.ifacol.2017.08.742.
    107. S. Linsenmayer und F. Allgöwer, „Stabilization of Networked Control Systems with weakly hard real-time dropout description“, in Proc. 56th IEEE Conf. Decision and Control (CDC), in Proc. 56th IEEE Conf. Decision and Control (CDC). Melbourne, Australia, 2017, S. 4765–4770. doi: 10.1109/CDC.2017.8264364.
    108. J. M. Montenbruck, S. Zeng, und F. Allgöwer, „Linear Systems with Quadratic Outputs“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). Seattle, WA, USA, 2017, S. 1030–1034.
    109. P. N. Köhler, M. A. Müller, und F. Allgöwer, „Transient performance of economic model predictive control with average constraints“, in Proc. 56th IEEE Conf. Decision and Control (CDC), in Proc. 56th IEEE Conf. Decision and Control (CDC). Melbourne, Victoria, Australia, 2017, S. 5557–5562.
    110. W. Halter, J. M. Montenbruck, und F. Allgöwer, „Systems with integral resource consumption“, in Proc. 56th IEEE Conf. Decision and Control (CDC), in Proc. 56th IEEE Conf. Decision and Control (CDC). Melbourne, Australia, 2017.
    111. P. N. Köhler, M. A. Müller, J. Pannek, und F. Allgöwer, „On Exploitation of Supply Chain Properties by Sequential Distributed MPC.“, in Proc. 20th IFAC World Congress, in Proc. 20th IFAC World Congress. Toulouse, France, 2017, S. 8219–8224.
    112. M. Lorenzen, M. A. Müller, und F. Allgöwer, „Stabilizing Stochastic MPC without Terminal Constraints“, in Proc. American Control Conf. (ACC), in Proc. American Control Conf. (ACC). Seattle, Washington, 2017, S. 5636–5641.
    113. A. Romer, J. M. Montenbruck, und F. Allgöwer, „Determining dissipation inequalities from input-output samples“, in Proc. 20th IFAC World Congress, in Proc. 20th IFAC World Congress. Toulouse, France, 2017, S. 7789–7794. doi: 10.1016/j.ifacol.2017.08.1053.

Frank Allgöwer ist Leiter des Instituts für Systemtheorie und Regelungstechnik an der Universität Stuttgart. Seine Forschungsschwerpunkte liegen in der Entwicklung von neuen Methoden der System- und Regelungstheorie mit speziellem Schwerpunkt auf der nichtlinearen, vernetzten, prädiktiven und datenbasierten Regelung; Anwendungsgebiete umfassen u.a. die verfahrenstechnische Prozessregelung, die Mechatronik, die biomedizinische Technik und die Nanotechnologie. Ein weiterer Forschungsschwerpunkt liegt auf dem Gebiet der Systembiologie.

Frank Allgöwer engagiert sich in ausgewählten Wissenschafts- und Universitätsgremien und nationalen und internationalen Organisationen und ist Herausgeber und Mitherausgeber diverser internationaler Fachzeitschriften. Er wurde mit verschiedenen Preisen ausgezeichnet, u.a. dem Gottfried-Wilhelm-Leibniz Preis der DFG (2004), dem Landeslehrpreis des Landes Baden-Württemberg (2007), dem IFAC Outstanding Service Award der International Federation auf Automatic Control (2011) und dem Distinguished Member Award der IEEE Control System Society (2015). Von 2012 bis 2020 war er Vizepräsident der Deutschen Forschungsgemeinschaft.

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Werdegang

23.05.1962                    Geboren in Heilbronn-Sontheim 

1981 – 1987                  Studium der Technischen Kybernetik und Angewandten
                                      Mathematik an der Universität Stuttgart bzw. University of
                                      California at Los Angeles (UCLA)

1988 – 1995                  Wissenschaftlicher Mitarbeiter am Institut für
                                      Systemdynamik und Regelungstechnik,
                                      Universität 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                              Promotion, Universität Stuttgart
                                      Titel: Näherungsweise Ein- / Ausgangslinearisierung
                                      nichtlinearer Systeme

1996 – 1999                  Assistenzprofessor für Nichtlineare Systeme, Departement
                                      Elektrotechnik, ETH Zürich, Schweiz

seit 1999                       Professor und Direktor des Instituts für Systemtheorie und
                                      Regelungstechnik, Universität Stuttgart

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

2010 – 2011                  Visiting Professor, University of Newcastle, Australien

2018                              Mitgründer, Spin-off TGU Systemwissenschaften

2019                              Mitgründer, Spin-off eStarling.io

Über 20 Einladungen zu Hauptvorträgen auf internationalen Konferenzen in den letzten fünf Jahren

2002                 NaT-Working Preis der Robert Bosch Stiftung

2004                 Gottfried-Wilhelm-Leibniz Preis der Deutschen 
                         Forschungsgemeinschaft

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                 Landeslehrpreis des Landes Baden-Württemberg

2008                 Best Paper Award, IFAC 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 (IFAC)

2011                 International Best Paper Award, SICE 2011

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

2013                 Preis für den besten Vortrag „Regelungstechnischen Kolloquium
                         Boppard"

2013                 DeGruyter Publishing Best Paper Award

2015                 Distinguished Member Award der IEEE Control System Society

2017                 Journal of Process Control Paper Prize Award

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

2017, 2019       Publikationspreis der Universität Stuttgart

2019                 Preis „Ideenwettbewerb: Mobilitätskonzepte für den emissions-
                         freien Campus“ des MWK Baden-Württemberg für Mobility Living
                         Lab (MobiLab)

2018                 Publikationspreis 2017 der Universität Stuttgart

2018                 Best Paper Award, 9th IFAC Symposium on Robust Control Design
                         (ROCOND'18)

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

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                 Publikationspreis 2019 der Universität Stuttgart

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

 

Entwicklung von neuen Methoden der System- und Regelungstheorie mit speziellem Schwerpunkt auf:

  • Nichtlineare Regelung
  • Vernetzte Regelung
  • Prädiktive Regelung
  • Datenbasierte Regelung

Anwendungsschwerpunkte:

  • Verfahrenstechnische Prozessregelung
  • Mechatronik
  • Biomedizinische Technik
  • Nanotechnologie
  • Systembiologie

Universitäts- und Wissenschaftsgremien

seit 1999           Studiendekan „Technische Kybernetik“, Universität Stuttgart

1999 - 2004       Stellvertretender Sprecher, SFB 412, Universität Stuttgart

2003 - 2012       Mitglied des Fachkollegiums „Systemtechnik“ der DFG

2005 - 2013       Mitglied des Direktoriums des „Zentrum Systembiologie“,
                          Universität Stuttgart

seit 2006            Vertrauensdozent der Studienstiftung des deutschen Volkes

2006 - 2012       Mitglied des Senatsausschusses Struktur, Universität Stuttgart

2006 – 2012      Mitglied der Ehrungskommission, Universität Stuttgart

seit 2007            Mitglied des Direktoriums der Exzellenzcluster „Simulation
                          Technology” (2007-2018) und „Data-integrated simulation science“
                          (seit 2019), Universität Stuttgart

seit 2009            Leiter der Graduiertenschule „Simulation Technology”, Universität
                          Stuttgart

seit 2009            Vorsitzender des Promotionsausschusses des Stuttgart Center for
                          Simulation Science

2009 – 2013      Mitglied im Vorstand des Informatik Verbund Stuttgart

2012 - 2017       Mitglied des Nominierungsausschusses für den Gottfried-Wilhelm-
                          Leibniz Preis der DFG

2012 - 2017       Vorsitzender der Jury für den Communicator Preis, Stifterverband
                          und DFG

2012 - 2020       Vizepräsident der Deutschen Forschungsgemeinschaft

2013                  „Sounding Board“ Ingenieurwissenschaften@2025 des
                          Ministeriums für Wissenschaft und Kunst, Baden-Württemberg

2013 – 2016      Mitglied im Beirat „Kerndatensatz“ des Wissenschaftsrats

2014 - 2018       Geschäftsführender Direktor des Stuttgart Research Centre
                          Systems Biology

2014 – 2015      Mitglied im Lenkungskreis der Evaluierung der Internationalen
                          Graduiertenkollegs der DFG 

2014 - 2020       Vorsitzender des Gemeinsamen Ausschuss für
                          Sicherheitsrelevante Forschung von Leopoldina und DFG

2015 – 2020      Sprecher der Forschungsinitiative „System Mensch“ der
                          Universitäten Stuttgart und Tübingen

seit 2017           Stellvertretender Sprecher der International Max-Planck Research
                          School for Intelligent Systems 

2018 -2020        Mitglied der Expertenkommissison „Wissenschaft im digitalen
                          Zeitalter“

seit 2018           Mitglied, Cyber Valley Plenary Assembly

seit 2019           Stellvertretender Sprecher des Exzellenzclusters „Data-integrated
                          simulation science“, Universität Stuttgart

seit 2019           Mitglied des Cyber Valley Research Fund Boards

2019 - 2020      Vorsitzender des NFDI-Expertengremiums der DFG

seit 2020           Mitglied der DFG Pandemiekommission

seit 2020           Gründungsmitglied, Interchange Forum for the Reflection of
                          Intelligent Systems

 

Verbände und Organisationen

seit 2000           Mitglied des Beirates der VDI/VDE-Gesellschaft Mess- und
                          Automatisierungstechnik (GMA)

2000 – 2015      Vorsitzender des Fachbereichs 1 „Grundlagen und Methoden der
                          Mess- und Automatisierungstechnik“ der 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

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

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

2011 – 2015      Chair, 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       Chair, Administration & Finance Committee, IFAC

2014 - 2017       Member, IEEE Control Systems Award Committee

2014 - 2017       Chair, Election Committee, IFAC

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

seit 2017            Member, IFAC Publication Management Board

seit 2020            Chair, Membership Committee, IFAC

 

Herausgeberschaften

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)

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

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

seit 1998            Mitglied in diversen Herausgeberbeiräten einschließlich
                           IEE Proceedings on Control Theory and Applications (seit 2006
                           IET), Journal of Nonlinear and Robust Control, Chemical
                           Engineering Science
, Canadian Journal of Chemical
                           Engineering, Control Handbook (CRC Press)

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