Communication Dans Un Congrès Année : 2024

Data-driven control of input saturated systems: a LMI-based approach

Résumé

This paper addresses three complex control challenges related to input-saturated systems from a data-driven perspective. Unlike the traditional two-stage process involving system identification and model-based control, the proposed approach eliminates the need for an explicit model description. The method combines data-based closed-loop representations, Lyapunov theory, instrumental variables, and a generalized sector condition to formulate data-driven linear matrix inequalities (LMIs). These LMIs are applied to maximize the origin’s basin of attraction, minimize the closed-loop reachable set with bounded disturbances, and introduce a new data-driven ℓ2-gain minimization problem. Demonstrations on benchmark examples highlight the advantages and limitations of the proposed approach compared to an explicit identification of the system, emphasizing notable benefits in handling nonlinear dynamics.
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Dates et versions

hal-04776126 , version 1 (10-01-2025)

Identifiants

Citer

F. Porcari, V. Breschi, Luca Zaccarian, S. Formentin. Data-driven control of input saturated systems: a LMI-based approach. In 20th IFAC Symposium on System Identification, Jul 2024, Boston (MA), United States. pp.205-210, ⟨10.1016/j.ifacol.2024.08.529⟩. ⟨hal-04776126⟩
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