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

TL;DR

This paper presents a new data-driven control method for unknown linear systems that guarantees stability using Lyapunov functions, combining static feedback and reference tracking, with demonstrated effectiveness through simulations.

Contribution

It introduces a novel data-driven control design with Lyapunov stability guarantees for unknown linear systems sharing the same order as the reference model.

Findings

Effective control design with stability guarantees

Robustness to measurement noise demonstrated

Outperforms existing approaches in simulations

Abstract

In this work, 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, which are 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 finally illustrated to show the potential of the proposed strategy as compared to the state of the art approaches.