Direct data-driven model-reference control with Lyapunov stability guarantees
Valentina Breschi, Claudio De Persis, Simone Formentin, Pietro Tesi
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.
Topics & Concepts
Control theory (sociology)Reference modelStability (learning theory)Computer scienceLyapunov functionNoise (video)State (computer science)Block (permutation group theory)Lyapunov stabilityControl (management)MathematicsNonlinear systemAlgorithmArtificial intelligenceImage (mathematics)Machine learningGeometryQuantum mechanicsPhysicsSoftware engineeringControl Systems and IdentificationAdvanced Control Systems OptimizationFault Detection and Control Systems