Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Valentina Breschi, Andrea Sassella, Simone Formentin

Abstract: In this letter, we propose a data-driven approach to derive explicit predictive control laws. The key idea of the presented strategy is to exploit the prior knowledge that the optimal solution is a piece-wise affine controller. As the proposed method allows us to automatically retrieve also a model of the closed-loop system, we show that we can apply classical Lyapunov techniques to perform a prior stability check for safe controller deployment. The effectiveness of the proposed strategy is assessed on a benchmark simulation example, through which we also discuss the use of regularization and preprocessing techniques to handle the presence of noise.