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.”
  2. sdsds 2022

    1. T. Martin, T. B. Schön, and F. Allgöwer, “Gaussian inference for data-driven state-feedback design of nonlinear systems.”
  3. 2022

    1. 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
    2. 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.
    3. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” 2022.
    4. 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, Bayreuth, Germany, 2022, pp. 906–911.
    5. 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
    6. 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.
    7. 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.
    8. A. Baier and S. Staab, “A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances.” 2022. doi: 10.18419/darus-2905.
  4. 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), Zürich, Switzerland, 2021, vol. 144, 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, 2021, vol. 54, no. 8, 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), 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), 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 J. Contr. Optim., 2021, [Online]. Available: https://arxiv.org/abs/2102.06520
    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), Cambridge (UK), 2021, pp. 11–19. doi: 10.1109/MRS50823.2021.9620683.
    8. 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.
    9. T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” 2021.
    10. 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.
    11. 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.
    12. 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.
    13. T. Holicki and C. W. Scherer, “Algorithm Design and Extremum Control:Convex Synthesis due to Plant Multiplier Commutation,” 2021.
    14. 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.
    15. 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.
    16. 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
    17. 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
    18. C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees,” 2021.
    19. C. Fiedler, C. W. Scherer, and S. Trimpe, “Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, no. 8, pp. 7439–7447. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/16912
    20. 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.
    21. 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, Padova, Italy, 2021, pp. 13–18. doi: https://doi.org/10.1016/j.ifacol.2021.08.327.
    22. H. Eschmann, “A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot.” DaRUS, 2021. doi: 10.18419/DARUS-1845.
    23. 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.
    24. H. Ebel and P. Eberhard, “Non-Prehensile Cooperative Object Transportation with Omnidirectional Mobile Robots: Organization, Control, Simulation, and Experimentation,” in Proceedings of the 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), Cambridge, UK, 2021, pp. 1–10. doi: 10.1109/MRS50823.2021.9620541.
    25. 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
    26. 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.
    27. 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.
    28. 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.
    29. 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
    30. 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.
  5. 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, “Lifting to Passivity for $H_2$-Gain-Scheduling Synthesis with Full Block Scalings,” in IFAC-PapersOnLine, 2020, vol. 53, no. 2, pp. 7292–7298. doi: 10.1016/j.ifacol.2020.12.570.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. T. Holicki and C. W. Scherer, “Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems,” in IFAC-PapersOnline, 2020, vol. 53, no. 2, pp. 7299–7304. doi: 10.1016/j.ifacol.2020.12.981.
    8. 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.
    9. 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
    10. J. Berberich, A. Koch, C. W. Scherer, and F. Allgower, “Robust data-driven state-feedback design,” in 2020 American Control Conference (ACC), Jul. 2020, pp. 1532–1538. doi: 10.23919/acc45564.2020.9147320.
    11. M. Barreau, C. W. Scherer, F. Gouaisbaut, and A. Seuret, “Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis,” 2020.
  6. 2019

    1. 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.
    2. 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.
    3. 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.
    4. A. Romer, S. Trimpe, and F. Allgöwer, “Data-driven inference of passivity properties via Gaussian process optimization,” in 2019 18th European Control Conference (ECC), 2019, pp. 29--35.
    5. 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.
    6. T. Holicki and C. W. Scherer, “A Homotopy Approach for Robust Output-Feedback Synthesis,” in Proc. 27th. Med. Conf. Control Autom., 2019, pp. 87–93. doi: 10.1109/MED.2019.8798536.
    7. T. Holicki and C. W. Scherer, “Stability Analysis and Output-Feedback Synthesis of Hybrid Systems Affected by Piecewise Constant Parameters via Dynamic Resetting Scalings,” Nonlinear Anal. Hybri., vol. 34, pp. 179–208, 2019, doi: https://doi.org/10.1016/j.nahs.2019.06.003.
    8. D. Driess, S. Schmitt, and M. Toussaint, “Active Inverse Model Learning with Error and Reachable Set Estimates.,” in IROS, 2019, pp. 1826--1833.
    9. G. Baggio, S. Zampieri, and C. W. Scherer, “Gramian Optimization with Input-Power Constraints,” in 2019 IEEE 58th Conference on Decision and Control (CDC), Dec. 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.

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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