1. Data-driven stabilization of input-saturated systems
- Author
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Valentina Breschi, Luca Zaccarian, Simone Formentin, Control Systems, Eindhoven University of Technology [Eindhoven] (TU/e), Dipartimento di Ingegneria Industriale [Trento], University of Trento [Trento], Équipe Méthodes et Algorithmes en Commande (LAAS-MAC), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Dipartimento di Electtronica, Informazione e Bioingegneria [Politecnico Milano] (POLIMI), Politecnico di Milano [Milan] (POLIMI), and ANR-18-CE40-0010,HANDY,Systèmes Dynamiques Hybrides et en Réseau(2018)
- Subjects
[SPI]Engineering Sciences [physics] ,Control and Optimization ,Data-driven control ,Control and Systems Engineering ,input saturation ,Linear systems ,State feedback ,Lyapunov methods - Abstract
International audience; We provide a data-driven stabilization approach for input-saturated systems with formal Lyapunov guarantees. Through a generalized sector condition, we propose a convex design algorithm based on linear matrix inequalities for obtaining a regionally stabilizing datadriven static state-feedback gain. Regional, rather than global, properties allow us to address non-exponentially stable plants, thereby making our design broad in terms of applicability. Moreover, we discuss consistency issues and introduce practical tools to deal with measurement noise. Numerical simulations show the effectiveness of our approach and its sensitivity to the features of the dataset.
- Published
- 2023