Publications of PN 4

  1. Preprint: arXiv:2211.05639, 2022

    1. T. Martin, T. B. Schön, and F. Allgöwer, “Gaussian inference for data-driven state-feedback design of nonlinear systems,” in 22nd IFAC World Congress (submitted), Preprint: arXiv:2211.05639, in 22nd IFAC World Congress (submitted), Preprint: arXiv:2211.05639.

  2. 2023

    1. M. M. Morato, T. Holicki, and C. W. Scherer, “Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers,” 2023, doi: 10.48550/ARXIV.2210.03712.
    2. T. Monninger et al., “SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks,” IEEE Robotics and Automation Letters, pp. 1–8, 2023, doi: 10.1109/LRA.2023.3234771.
    3. 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, no. 5, Art. no. 5, 2023, doi: 10.1109/TAC.2022.3226652.
    4. A. Kharitenko and C. Scherer, “Time-varying Zames–Falb multipliers for LTI Systems are superfluous,” Automatica, vol. 147, p. 110577, Jan. 2023, doi: 10.1016/j.automatica.2022.110577.
    5. T. Holicki and C. W. Scherer, “IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties.” 2023. doi: 10.48550/arXiv.2212.08837.
    6. T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting.” 2023. doi: 10.48550/arXiv.2211.02149.
    7. A. Baier and D. Frank, “deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning.” DaRUS, 2023. doi: 10.18419/DARUS-3455.
    8. A. Baier, D. Aspandi, and S. Staab, “Supplements for ‘ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks’".” DaRUS, 2023. doi: 10.18419/DARUS-3457.
    9. 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.

  3. sdsds 2022

    1. T. Martin, T. B. Schön, and F. Allgöwer, “Gaussian inference for data-driven state-feedback design of nonlinear systems,” in 22nd IFAC World Congress (submitted), Preprint: arXiv:2211.05639, in 22nd IFAC World Congress (submitted), Preprint: arXiv:2211.05639.

  4. 2022

    1. P. Schmid, H. Ebel, and P. Eberhard, “Dependable Data-based Design of Embedded Model Predictive Control,” in 2022 European Control Conference (ECC), in 2022 European Control Conference (ECC). 2022, pp. 859–866. doi: 10.23919/ECC55457.2022.9838194.
    2. C. W. Scherer, “Dissipativity, Convexity and Tight O\textquotesingleShea-Zames-Falb Multipliers for Safety Guarantees,” IFAC-PapersOnLine, vol. 55, no. 30, Art. no. 30, 2022, doi: 10.1016/j.ifacol.2022.11.044.
    3. C. Scherer, “Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections,” IEEE Control Systems Magazine (to appear), 2022, [Online]. Available: https://arxiv.org/abs/2105.07401
    4. C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Networked Systems with Full Block Scalings,” Oct. 2022.
    5. M. Rosenfelder, H. Ebel, J. Krauspenhaar, and P. Eberhard, “Model Predictive Control of Non-Holonomic Vehicles: Beyond Differential-Drive,” 2022. [Online]. Available: https://arxiv.org/abs/2205.11400
    6. M. Rosenfelder, H. Ebel, and P. Eberhard, “Cooperative distributed nonlinear model predictive control of a formation of differentially-driven mobile robots,” Robotics and autonomous systems, vol. 150, no. April, Art. no. April, 2022, doi: https://doi.org/10.1016/j.robot.2021.103993.
    7. M. Rosenfelder, H. Ebel, and P. Eberhard, “A Force-Based Control Approach for the Non-Prehensile Cooperative Transportation of Objects Using Omnidirectional Mobile Robots,” in 2022 IEEE Conference on Control Technology and Applications (CCTA), in 2022 IEEE Conference on Control Technology and Applications (CCTA). 2022, pp. 349–356. doi: 10.1109/CCTA49430.2022.9966052.
    8. J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger, “Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,” IFAC-PapersOnLine, vol. 55, no. 20, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.
    9. 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), in Proc. 61st IEEE Conf. Decision and Control (CDC). Cancun, Mexico, 2022, pp. 1816–1822. doi: 10.1109/CDC51059.2022.9992740.
    10. 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), in Proc. 61st IEEE Conf. on Decision and Control (CDC). Cancun, Mexico, 2022, pp. 441–446. doi: 10.1109/CDC51059.2022.9993301.
    11. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” IEEE Trans. Automat. Control (accepted), Preprint:  arXiv:2108.11298, 2022.
    12. T. Martin and 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, pp. 1432–1437.
    13. W. Luo, H. Eschmann, and P. Eberhard, “Gaussian Process Regression-augmented Nonlinear Model Predictive Control for Quadrotor Object Grasping,” in International Conference on Unmanned Aircraft Systems (ICUAS22), in International Conference on Unmanned Aircraft Systems (ICUAS22), vol. Vol. 16. 2022, pp. 11–19. doi: 10.1109/ICUAS54217.2022.9836200.
    14. W. Luo, H. Ebel, and P. Eberhard, “An LSTM-based Approach to Precise Landing of a UAV on a Moving Platform,” International Journal of Mechanical System Dynamics, vol. 00, pp. 1–12, 2022.
    15. W. Luo, J. Chen, H. Ebel, and P. Eberhard, “Time-Optimal Handover Trajectory Planning for Aerial Manipulators based on Discrete Mechanics and Complementarity Constraints,” 2022. [Online]. Available: https://doi.org/10.48550/arXiv.2209.00533
    16. M. Köhler, J. Berberich, M. A. Müller, and F. Allgöwer, “Data-driven distributed MPC of dynamically coupled linear systems,” in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems, in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems. Bayreuth, Germany, 2022, pp. 906–911.
    17. A. Kharitenko and C. W. Scherer, “On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities,” Oct. 2022.
    18. T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting,” Nov. 2022.
    19. T. Holicki and C. W. Scherer, “IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties,” Dec. 2022.
    20. T. Holicki and C. W. Scherer, “A Dynamic S-Procedure for Dynamic Uncertainties,” in IFAC-PapersOnline, in IFAC-PapersOnline, vol. 55. 2022, pp. 103–108. doi: 10.1016/j.ifacol.2022.09.331.
    21. B. Hillebrecht and B. Unger, “Certified machine learning: A posteriori error estimation for physics-informed neural networks,” ArXiv e-print 2203.17055, 2022, [Online]. Available: http://arxiv.org/abs/2203.17055
    22. B. Hillebrecht and B. Unger, “Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs,” 2022.
    23. D. Gramlich, C. W. Scherer, and C. Ebenbauer, “Robust Differential Dynamic Programming,” in 61st IEEE Conf. Decision and Control, in 61st IEEE Conf. Decision and Control. 2022. doi: 10.1109/cdc51059.2022.9992569.
    24. D. Gramlich, C. Ebenbauer, and C. W. Scherer, “Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions,” Syst. Control Lett., vol. 165, 2022, [Online]. Available: https://arxiv.org/abs/2006.09946
    25. D. Frank, D. A. Latif, M. Muehlebach, B. Unger, and S. Staab, “Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees,” Publication, 2022. doi: 10.48550/arXiv.2212.05781.
    26. D. Frank, D. A. Latif, M. Muehlebach, and S. Staab, “Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report.” 2022. doi: https://doi.org/10.48550/arXiv.2212.05781.
    27. C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning Functions and Uncertainty Sets Using Geometrically Constrained Kernel Regression,” in 61st IEEE Conf. Decision and Control, in 61st IEEE Conf. Decision and Control. IEEE, Dec. 2022. doi: 10.1109/cdc51059.2022.9993144.
    28. H. Eschmann, H. Ebel, and P. Eberhard, “Exploration-Exploitation-Based Trajectory Tracking of Mobile Robots Using Gaussian Processes and Model Predictive Control,” 2022.
    29. H. Eschmann, H. Ebel, and P. Eberhard, “High Accuracy Data-Based Trajectory Tracking of a Mobile Robot,” in Advances in Service and Industrial Robotics, in Advances in Service and Industrial Robotics, vol. 31. 2022, pp. 420–427. doi: https://doi.org/10.1007/978-3-031-04870-8_49.
    30. H. Ebel, D. N. Fahse, M. Rosenfelder, and P. Eberhard, “Finding Formations for the Non-prehensile Object Transportation with Differentially-Driven Mobile Robots,” in CISM International Centre for Mechanical Sciences book series, in CISM International Centre for Mechanical Sciences book series, vol. 606. 2022, pp. 163–170. doi: https://doi.org/10.1007/978-3-031-06409-8_17.
    31. H. Ebel and P. Eberhard, “Cooperative transportation: realizing the promises of robotic networks using a tailored software/hardware architecture,” at - Automatisierungstechnik, vol. 70, no. 4, Art. no. 4, 2022, doi: doi:10.1515/auto-2021-0105.
    32. T. Breiten and B. Unger, “Passivity preserving model reduction via spectral factorization,” Automatica, vol. 142, p. 110368, Aug. 2022, doi: 10.1016/j.automatica.2022.110368.
    33. T. Breiten, D. Hinsen, and B. Unger, “Towards a modeling class for port-Hamiltonian systems with time-delay,” 2022. doi: 10.48550/arXiv.2211.10687.
    34. J. Berberich, C. W. Scherer, and F. Allgower, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, pp. 1--16, 2022, doi: 10.1109/tac.2022.3209342.
    35. 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), in Proc. 61st IEEE Conf. Decision and Control (CDC). Cancun, Mexico, 2022, pp. 1105–1110. doi: 10.1109/CDC51059.2022.9993361.
    36. 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, no. 9, Art. no. 9, 2022, doi: 10.1109/TAC.2022.3166851.
    37. 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, no. 9, Art. no. 9, 2022, doi: 10.1109/TAC.2022.3166872.
    38. A. Baier and S. Staab, “A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances.” 2022. doi: 10.18419/darus-2905.

  5. 2021

    1. 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.
    2. N. Wieler, J. Berberich, A. Koch, and F. Allgöwer, “Data-driven controller design via finite-horizon dissipativity,” in Proc. 3rd Learning for Dynamics and Control Conf. (L4DC), in Proc. 3rd Learning for Dynamics and Control Conf. (L4DC), vol. 144. Zürich, Switzerland: PMLR, 2021, pp. 287–298.
    3. J. Veenman, C. W. Scherer, C. Ardura, S. Bennani, V. Preda, and B. Girouart, “IQClab: A new IQC based toolbox for robustness analysis and control design,” in IFAC-PapersOnLine, in IFAC-PapersOnLine, vol. 54. Elsevier BV, 2021, pp. 69--74. doi: 10.1016/j.ifacol.2021.08.583.
    4. 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), in Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, pp. 4344–4351. doi: 10.1109/CDC45484.2021.9683211.
    5. 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), in Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, pp. 4882–4887. doi: 10.1109/CDC45484.2021.9683026.
    6. C. Scherer and C. Ebenbauer, “Convex Synthesis of Accelerated Gradient Algorithms,” SIAM Journal on Control and Optimization, vol. 59, no. 6, Art. no. 6, 2021, doi: 10.1137/21M1398598.
    7. M. Rosenfelder, H. Ebel, and P. Eberhard, “Cooperative Distributed Model Predictive Formation Control of Non-Holonomic Robotic Agents,” in Proceedings of the 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), in Proceedings of the 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). Cambridge (UK), 2021, pp. 11–19. doi: 10.1109/MRS50823.2021.9620683.
    8. M. I. Müller, A. Koch, F. Allgöwer, and C. R. Rojas, “Data-Driven Input-Passivity Estimation Using Power Iterations,” IFAC-PapersOnLine, vol. 54, no. 7, Art. no. 7, 2021, doi: https://doi.org/10.1016/j.ifacol.2021.08.429.
    9. S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach,” International Journal of Control, vol. 94, no. 11, Art. no. 11, 2021, doi: 10.1080/00207179.2020.1745286.
    10. 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 (early access), 2021, doi: 10.1109/TAC.2022.3226652.
    11. T. Martin and F. Allgöwer, “Dissipativity Verification With Guarantees for Polynomial Systems From Noisy Input-State Data,” IEEE Control Systems Letters, vol. 5, no. 4, Art. no. 4, Oct. 2021, doi: 10.1109/LCSYS.2020.3037842.
    12. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” in Preprint:  arXiv:2108.11298, in Preprint:  arXiv:2108.11298,. 2021.
    13. W. Luo, H. Ebel, and P. Eberhard, “An LSTM-based Approach to Precise Landing of a UAV on a Moving Platform,” International Journal of Mechanical System Dynamics, vol. 00, pp. 1–12, 2021.
    14. 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.
    15. A. Koch, J. Berberich, and F. Allgöwer, “Provably robust verification of dissipativity properties from data,” IEEE Transactions on Automatic Control, vol. 67, no. 8, Art. no. 8, 2021, doi: 10.1109/TAC.2021.3116179.
    16. T. Holicki and C. W. Scherer, “Revisiting and Generalizing the Dual Iteration for Static and Robust Output-Feedback Synthesis,” Int. J. Robust Nonlin., pp. 1–33, 2021, doi: 10.1002/rnc.5547.
    17. T. Holicki and C. W. Scherer, “Algorithm Design and Extremum Control:Convex Synthesis due to Plant Multiplier Commutation,” in 60th IEEE Conf. Decision and Control, in 60th IEEE Conf. Decision and Control. 2021.
    18. T. Holicki, C. W. Scherer, and S. Trimpe, “Controller Design via Experimental Exploration with Robustness Guarantees,” IEEE Control Syst. Lett., vol. 5, no. 2, Art. no. 2, 2021, doi: 10.1109/LCSYS.2020.3004506.
    19. T. Holicki and C. W. Scherer, “Robust Gain-Scheduled Estimation with Dynamic D-Scalings,” IEEE Trans. Autom. Control, vol. 66, no. 11, Art. no. 11, 2021, doi: 10.1109/TAC.2021.3052751.
    20. D. Gramlich, C. Ebenbauer, and C. W. Scherer, “Convex Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions,” accepted for Syst. Control Lett., 2021, [Online]. Available: https://arxiv.org/abs/2006.09946
    21. I. V. Gosea, S. Gugercin, and B. Unger, “Parametric model reduction via rational interpolation along parameters,” ArXiv e-print 2104.01016, 2021, [Online]. Available: https://arxiv.org/abs/2104.01016
    22. C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees,” in 60th IEEE Conf. Decision and Control, in 60th IEEE Conf. Decision and Control. 2021.
    23. C. Fiedler, C. W. Scherer, and S. Trimpe, “Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, Art. no. 8, 2021.
    24. H. Eschmann and P. Eberhard, “Learning-Based Model Predictive Control for Multi-Agent Systems using Gaussian Processes,” PAMM, vol. 20, no. 1, Art. no. 1, 2021, doi: https://doi.org/10.1002/pamm.202000009.
    25. H. Eschmann, H. Ebel, and P. Eberhard, “Trajectory tracking of an omnidirectional mobile robot using Gaussian process regression,” at - Automatisierungstechnik, vol. 69, no. 8, Art. no. 8, 2021, doi: doi:10.1515/auto-2021-0019.
    26. H. Eschmann, H. Ebel, and P. Eberhard, “Data-Based Model of an Omnidirectional Mobile Robot Using Gaussian Processes,” in IFAC Symposium on System Identification (SYSID) - Learning models for decision and control, in IFAC Symposium on System Identification (SYSID) - Learning models for decision and control. Padova, Italy, 2021, pp. 13–18. doi: https://doi.org/10.1016/j.ifacol.2021.08.327.
    27. H. Eschmann, “A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot.” DaRUS, 2021. doi: 10.18419/DARUS-1845.
    28. H. Ebel and P. Eberhard, “Non-Prehensile Cooperative Object Transportation with Omnidirectional Mobile Robots: Organization, Control, Simulation, and Experimentation,” in 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), in 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). Cambridge, UK, 2021, pp. 1–10. doi: 10.1109/MRS50823.2021.9620541.
    29. T. Breiten and B. Unger, “Passivity preserving model reduction via spectral factorization,” ArXiv e-print 2103.13194, 2021, [Online]. Available: https://arxiv.org/abs/2103.13194
    30. 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), in Proc. 19th IFAC Symp. System Identification (SYSID). Padova, Italy, 2021, pp. 210–215. doi: 10.1016/j.ifacol.2021.08.360.
    31. J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-driven model predictive control: closed-loop guarantees and experimental results,” at-Automatisierungstechnik, vol. 69, no. 7, Art. no. 7, 2021, doi: 10.1515/auto-2021-0024.
    32. 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), in Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC). Bratislava, Slovakia, 2021, pp. 257–263. doi: 10.1016/j.ifacol.2021.08.554.
    33. A. Baier, Z. Boukhers, and S. Staab, “Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction,” 2021. [Online]. Available: http://arxiv.org/abs/2103.06727
    34. 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), in Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, pp. 1484–1489. doi: 10.1109/CDC45484.2021.9683327.
    35. 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 the 3rd Conference on Learning for Dynamics and Control, vol. 144. 2021, pp. 163--175.

  6. 2020

    1. I. Wochner, D. Driess, H. Zimmermann, D. F. Haeufle, M. Toussaint, and S. Schmitt, “Optimality principles in human point-to-manifold reaching accounting for muscle dynamics,” Frontiers in Computational Neuroscience, vol. 14, p. 38, 2020.
    2. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, no. 1, Art. no. 1, 2020, doi: 10.1109/TCNS.2019.2914411.
    3. C. A. Rösinger and C. W. Scherer, “Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings,” in IFAC-PapersOnLine, in IFAC-PapersOnLine, vol. 53. 2020, pp. 7292–7298. doi: 10.1016/j.ifacol.2020.12.570.
    4. S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms : an integral quadratic constraint approach,” International Journal of Control, vol. 2020, pp. 1–24, 2020, doi: 10.1080/00207179.2020.1745286.
    5. S. Michalowsky, C. W. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimization algorithms: An integral quadratic constraint approach,” Int. J. Control, pp. 1–24, 2020, doi: 10.1080/00207179.2020.1745286.
    6. T. Martin, A. Koch, and 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, pp. 971–976. doi: 10.1016/j.ifacol.2020.12.1261.
    7. 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), in Proc. 59th IEEE Conf. Decision and Control (CDC). Jeju, South Korea, 2020, pp. 4760–4765. doi: 10.1109/CDC42340.2020.9304285.
    8. T. Holicki and C. W. Scherer, “Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems,” in IFAC-PapersOnline, in IFAC-PapersOnline, vol. 53. 2020, pp. 7299–7304. doi: 10.1016/j.ifacol.2020.12.981.
    9. 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), 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.
    10. J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining Prior Knowledge and Data for Robust Controller Design,” 2020, [Online]. Available: https://arxiv.org/abs/2009.05253
    11. J. Berberich, A. Koch, C. W. Scherer, and F. Allgower, “Robust data-driven state-feedback design,” in 2020 American Control Conference (ACC), in 2020 American Control Conference (ACC). IEEE, Jul. 2020, pp. 1532–1538. doi: 10.23919/acc45564.2020.9147320.
    12. M. Barreau, C. W. Scherer, F. Gouaisbaut, and A. Seuret, “Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis,” in IFAC World Congress, in IFAC World Congress. 2020.

  7. 2019

    1. C. A. Rösinger and C. W. Scherer, “A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination,” IFAC-PapersOnLine, vol. 52, no. 28, Art. no. 28, 2019, doi: 10.1016/j.ifacol.2019.12.347.
    2. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, no. 1, Art. no. 1, 2019, doi: 10.1109/TCNS.2019.2914411.
    3. A. Romer, S. Trimpe, and F. Allgöwer, “Data-driven inference of passivity properties via Gaussian process optimization,” in 2019 18th European Control Conference (ECC), in 2019 18th European Control Conference (ECC). IEEE, 2019, pp. 29--35.
    4. A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input--output data,” IEEE Control Systems Letters, vol. 3, no. 3, Art. no. 3, 2019.
    5. T. Martin and 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, pp. 3605–3610. doi: 10.1109/CDC40024.2019.9029804.
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Project Network Coordinators

This image shows Frank Allgöwer

Frank Allgöwer

Prof. Dr.-Ing.

[Photo: SimTech/Max Kovalenko]

This image shows Peter Eberhard

Peter Eberhard

Prof. Dr.-Ing. Prof. E.h.

[Photo: SimTech/Max Kovalenko]

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