A Bi-level Approach to MPC for Switching Nonlinear Systems

Abstract

In this paper, a nonlinear model predictive control scheme for switching dynamical systems is presented. The controller comprises of two layers of optimization. The upper layer is based on the embedding transformation technique, hence it does not require prior knowledge of the switching sequence. In particular, it provides the optimal relaxed switching sequence together with the optimal regulating inputs and the corresponding trajectories of the states. Within the lower layer, the integrality constraints are restored and a switching solution is recomputed to minimize the error with respect to the trajectories given from the upper-level optimization. The scheme is presented and the bounds of the integer approximation errors are evaluated together with brief recursive feasibility analysis. Simulation results of a tracking and an economics optimizing nonlinear MPC for a supermarket refrigerator system show the applicability and efficiency of the proposed approach.