A Data-Driven MPC for a Class of Nonlinear Discrete-Time Systems

In this letter, a novel data-driven Model Predictive Control scheme is conceived for a class of constrained discrete-time nonlinear systems. By taking advantage of the capabilities made available by behavioral approach, reinforcement learning and set-theoretic ideas, the receding horizon controller is formally derived by customizing reachability analysis to the data-driven scenario. The constrained optimization problem is formulated through Fundamental Lemma arguments applied to local linear descriptions of the nonlinear dynamics.