We introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is composed by a static feedback term and a reference tracking block shaped from data to reproduce the desired behavior in closed-loop. By focusing on the case where the reference model and the plant share the same order, we propose an optimal design procedure with Lyapunov stability guarantees, tailored to handle state measurements with additive noise. Two simulation examples are illustrated to show the potential of the proposed strategy.

Direct data-driven model-reference control with Lyapunov stability guarantees

Breschi V.;Formentin S.;
2021

Abstract

We introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state. The proposed control action is composed by a static feedback term and a reference tracking block shaped from data to reproduce the desired behavior in closed-loop. By focusing on the case where the reference model and the plant share the same order, we propose an optimal design procedure with Lyapunov stability guarantees, tailored to handle state measurements with additive noise. Two simulation examples are illustrated to show the potential of the proposed strategy.
Proceedings of the IEEE Conference on Decision and Control
978-1-6654-3659-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1209186
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