Data-enhanced analysis and controller design for uncertain systems

PN 4-3 (II)

Project description

Modern robust control offers computational techniques for analyzing dynamical systems affected by uncertainties and for synthesizing controllers accordingly. These rely on linear fractional representations in which uncertainties are interpreted as dynamical systems with inputs and outputs and on dissipation constraints for describing the behavior of these systems. However, even when employing advanced tools from dissipation theory, the involved uncertainty descriptions mostly do not take experimental data of the underlying system into account. In the first phase of PN 4-3 we tackled this gap from several angles and in tight collaboration with PN 4-1 and PN 4-2. We established novel practical and rigorous frequentist uncertainty bounds for Gaussian processes and embedded these successfully into the robust control framework. This enables the combination with deterministic methods for designing robust controllers with probabilistic guarantees. Moreover, we developed novel robust design techniques for truly mixed models combining prior structural knowledge with data obtained from experiments. They combine a rough uncertainty description with a single noisy input-state trajectory in order to generate a state-feedback controller assuring stability and performance, even on an infinite time horizon. In the second phase of this project, we aim to overcome several still existing restrictions. As a major step forward, we target at employing dynamic supply rates in the dissipation framework. Instead of the currently used rather crude static version for parametric and nonlinear uncertainties. Moreover, we will remove the requirement that measurements of the full system state are available.  Finally, we will investigate how to exploit the so-called scheduled controller design framework for synthesizing data-integrated feedback controllers that learn and adapt themselves to batches of data gathered during the operation of the closed-loop.

Project information

Project title Data-enhanced analysis and controller design for uncertain systems
Project leader Carsten Scherer
Collaborative applicant Peter Eberhard, Andrea Iannelli
Project staff Shuhao Yan, postdoctoral researcher
Project duration September 2022 - December 2025
Project number PN 4-3 (II)

Publications PN 4-3 and PN 4-3 (II)

  1. 2024

    1. A. Kharitenko and C. W. Scherer, “On the exactness of a stability test for Lur’e systems with slope-restricted nonlinearities,” IEEE Transactions on Automatic Control, 2024, doi: 10.1109/TAC.2024.3362859.
    2. T. J. Meijer, T. Holicki, S. J. A. M. v. d. Eijnden, C. W. Scherer, and W. P. M. H. Heemels, “The Non-Strict Projection Lemma,” IEEE Transactions on Automatic Control, pp. 1–8, 2024, doi: 10.1109/TAC.2024.3371374.

  2. 2023

    1. T. Holicki, J. Nicodemus, P. Schwerdtner, and B. Unger, “Energy matching in reduced passive and port-Hamiltonian systems.” 2023. doi: 10.48550/arXiv.2309.05778.
    2. F. A. Taha, S. Yan, and E. Bitar, “A Distributionally Robust Approach to Regret Optimal Control using the Wasserstein Distance,” in 2023 62nd IEEE Conference on Decision and Control (CDC), 2023, pp. 2768–2775. doi: 10.1109/CDC49753.2023.10384311.
    3. L. Hewing et al., “Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods,” in Papers of ESA GNC-ICATT 2023, in ESA GNC-ICATT 2023. ESA, Jul. 2023. doi: 10.5270/esa-gnc-icatt-2023-135.
    4. T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.047.
    5. C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Nested Systems With Full Block Scalings,” IEEE Transactions on Automatic Control, pp. 1–16, 2023, doi: 10.1109/TAC.2023.3329851.
    6. T. Holicki and C. W. Scherer, “IQC based analysis and estimator design for discrete-time systems affected by impulsive uncertainties,” Nonlinear Analysis: Hybrid Systems, vol. 50, p. 101399, Nov. 2023, doi: 10.1016/j.nahs.2023.101399.
    7. M. M. Morato, T. Holicki, and C. W. Scherer, “Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers,” International Journal of Robust and Nonlinear Control, vol. 33, Art. no. 18, 2023, doi: https://doi.org/10.1002/rnc.6952.
    8. D. Gramlich, T. Holicki, C. W. Scherer, and C. Ebenbauer, “A Structure Exploiting SDP Solver for Robust Controller Synthesis,” IEEE Control Syst. Lett., vol. 7, pp. 1831–1836, 2023, doi: 10.1109/LCSYS.2023.3277314.
    9. 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.
    10. C. W. Scherer, “Robust Exponential Stability and Invariance Guarantees with General Dynamic O’Shea-Zames-Falb Multipliers,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.556.
    11. J. Berberich, C. W. Scherer, and F. Allgower, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 8, 2023, doi: 10.1109/tac.2022.3209342.

  3. 2022

    1. T. Holicki, “A Complete Analysis and Design Framework for Linear Impulsive and Related Hybrid Systems,” University of Stuttgart, 2022. doi: 10.18419/opus-12158.
    2. D. Gramlich, C. W. Scherer, and C. Ebenbauer, “Robust Differential Dynamic Programming,” in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022. doi: 10.1109/cdc51059.2022.9992569.
    3. 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, IEEE, Dec. 2022. doi: 10.1109/cdc51059.2022.9993144.
    4. D. Gramlich, C. Ebenbauer, and C. W. Scherer, “Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions,” Systems & Control Letters, vol. 165, 2022, doi: 10.1016/j.sysconle.2022.105271.
    5. J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 8, 2022, doi: 10.1109/tac.2022.3209342.
    6. C. Scherer, “Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections,” IEEE Control Systems Magazine, vol. 42, Art. no. 3, 2022, [Online]. Available: https://arxiv.org/abs/2105.07401

  4. 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, pp. 69–74. doi: 10.1016/j.ifacol.2021.08.583.
    2. C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees,” in 60th IEEE Conference Decision and Control, 2021.
    3. T. Holicki and C. W. Scherer, “Algorithm Design and Extremum Control: Convex Synthesis due to Plant Multiplier Commutation,” in Proc. 60th IEEE Conf. Decision and Control, 2021, pp. 3249–3256. doi: 10.1109/CDC45484.2021.9683012.
    4. S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach,” International Journal of Control, vol. 94, Art. no. 11, 2021, doi: 10.1080/00207179.2020.1745286.
    5. T. Holicki, C. W. Scherer, and S. Trimpe, “Controller Design via Experimental Exploration with Robustness Guarantees,” IEEE Control Systems Letters, vol. 5, Art. no. 2, 2021, doi: 10.1109/LCSYS.2020.3004506.
    6. T. Holicki and C. W. Scherer, “Robust Gain-Scheduled Estimation with Dynamic D-Scalings,” EEE Transactions on Automatic Control, vol. 66, Art. no. 11, 2021, doi: 10.1109/TAC.2021.3052751.
    7. C. Scherer and C. Ebenbauer, “Convex Synthesis of Accelerated Gradient Algorithms,” SIAM Journal on Control and Optimization, vol. 59, Art. no. 6, 2021, doi: 10.1137/21M1398598.
    8. T. Holicki and C. W. Scherer, “Revisiting and Generalizing the Dual Iteration for Static and Robust Output-Feedback Synthesis,” Int. J. Robust Nonlin., vol. 31, Art. no. 11, 2021, doi: 10.1002/rnc.5547.

  5. 2020

    1. T. Holicki and C. W. Scherer, “Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems,” in IFAC-PapersOnline, 2020, pp. 7299–7304. doi: 10.1016/j.ifacol.2020.12.981.
    2. M. Barreau, C. W. Scherer, F. Gouaisbaut, and A. Seuret, “Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis,” in IFAC-PapersOnline, 2020, pp. 7752–7757. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896320321297
    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. C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, Art. no. 1, 2020, doi: 10.1109/TCNS.2019.2914411.

  6. 2019

    1. 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.
    2. G. Baggio, S. Zampieri, and C. W. Scherer, “Gramian Optimization with Input-Power Constraints,” in 58th IEEE Conf. Decision and Control, 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.
    3. C. A. Rösinger and C. W. Scherer, “A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination,” in IFAC-PapersOnLine, 2019, pp. 50–57. doi: 10.1016/j.ifacol.2019.12.347.
    4. 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 Analysis: Hybrid Systems, vol. 34, pp. 179–208, 2019, doi: https://doi.org/10.1016/j.nahs.2019.06.003.
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