Publications of PN 4

  1. 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. 2021

    1. 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.
    2. C. Scherer and C. Ebenbauer, “Convex Synthesis of Accelerated Gradient Algorithms,” SIAM J. Contr. Optim., 2021, [Online]. Available: https://arxiv.org/abs/2102.06520
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. T. Holicki and C. W. Scherer, “Algorithm Design and Extremum Control:Convex Synthesis due to Plant Multiplier Commutation,” 2021.
    8. 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.
    9. 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.
    10. 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
    11. 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
    12. 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.
    13. C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees,” 2021.
    14. 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.
    15. 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.
    16. H. Eschmann, “A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot.” DaRUS, 2021. doi: 10.18419/DARUS-1845.
    17. 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.
    18. 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.
    19. 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
  3. 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. 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
    9. 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.
    10. M. Barreau, C. W. Scherer, F. Gouaisbaut, and A. Seuret, “Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis,” 2020.
  4. 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,” in IFAC-PapersOnLine, 2019, vol. 52, no. 28, pp. 50–57. 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 Carsten W. Scherer

Carsten W. Scherer

Prof. Dr.

[Photo: SimTech/Max Kovalenko]

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