Optimization-based design of data-integrated controllers

PN 4-3

Project description

Modern control theory offers abundant methods for the design of feedback loops that enforce a certain desired behaviour with mathematical guarantees. Remarkably, proofs of stability and performance in many if not all these controller design strategies are based on dissipativity theory as is well-developed for first-principles models of dynamical systems. In view of its trajectory-interpretation, the dissipativity approach offers an ideal setting for handling dynamical systems that are represented by first principles and input-output data, as investigated in PN 4-1. In this project we target at expanding controller synthesis methods to systems that are described by models with data integration and with a specific emphasis on guarantees for robustness of stability and performance. Moreover, in extending the scheduling approach to control, which is so far mostly limited to adaptations in reaction to time-varying parametric changes, new techniques will allow the design of controllers that are capable of adapting structurally to newly incoming online data or even to changes in the dynamics, all equipped with mathematical certificates for the controlled system. First steps will be taken in pursing the longer-term goal to enhance these offline designs with online learning capabilities and with guarantees for the stable operation of the resulting adaptive mechanisms.

Project information

Project title Optimization-based design of data-integrated controllers
Project leaders Carsten Scherer (Sebastian Trimpe)
Project duration October 2019 - March 2023
Project number PN 4-3

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

  1. 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. 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.
    3. 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, 2023, doi: 10.1002/rnc.6952.
    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,” Nonlinear Analysis: Hybrid Systems, vol. 50, p. 101399, 2023, doi: 10.1016/j.nahs.2023.101399.

  2. 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), 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, 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, no. 8, 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, no. 3, Art. no. 3, 2022, [Online]. Available: https://arxiv.org/abs/2105.07401

  3. 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, in IFAC-PapersOnline, vol. 54. 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, in 60th IEEE Conference Decision and Control. 2021.
    3. 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.
    4. T. Holicki, C. W. Scherer, and S. Trimpe, “Controller Design via Experimental Exploration with Robustness Guarantees,” IEEE Control Systems Letters, vol. 5, no. 2, Art. no. 2, 2021, doi: 10.1109/LCSYS.2020.3004506.
    5. T. Holicki and C. W. Scherer, “Robust Gain-Scheduled Estimation with Dynamic D-Scalings,” EEE Transactions on Automatic Control, vol. 66, no. 11, Art. no. 11, 2021, doi: 10.1109/TAC.2021.3052751.
    6. 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, no. 11, Art. no. 11, 2021, doi: 10.1002/rnc.5547.

  4. 2020

    1. 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, in IFAC-PapersOnline, vol. 53. 2020, pp. 7752–7757. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896320321297
    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.

  5. 2019

    1. T. Holicki and C. W. Scherer, “A Homotopy Approach for Robust Output-Feedback Synthesis,” in Proc. 27th. Med. Conf. Control Autom., 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, in 58th IEEE Conf. Decision and Control. 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.
    3. 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.
To the top of the page