Direct data-driven design of switching controllers for constrained systems

This paper presents a hierarchical structure to directly design controllers for (possibly nonlinear) constrained systems. The proposed architecture combines the advantages of an inner data-driven switching controller designed to achieve a predefined closed-loop behavior and an outer model predictive controller, which is used as a reference governor. These design choices enable us to avoid the identification step typical of model-based approaches while exploiting the ability of model predictive controllers to handle constraints and optimize the closed-loop performance. As a proof of concept, a benchmark simulation example is used to demonstrate the effectiveness of the proposed strategy.