Data-driven design of explicit predictive controllers

Andrea Sassella, Valentina Breschi, Simone Formentin

Abstract: In this paper, we propose an approach to map experimental measurements directly onto explicit predictive control laws, without the need of explicitly deriving the system matrices first. To this aim, we resort to the Willems’ fundamental lemma, through which we obtain the explicit formulas by suitably elaborating the resulting constrained optimization problem. To tackle the pervading presence of noise in real data, we propose a noise handling strategy guaranteeing to recover the noiseless performance asymptotically. The effectiveness of the proposed strategy is supported by numerical simulations on two benchmark examples.