Improved MPC design for constrained linear periodic systems

This paper is concerned with a design of stabilizing model predictive control laws for discrete-time linear periodic systems with state and control constraints. Two algorithms are presented. The first one is based on interpolation between several unconstrained periodic controllers. Among them, one controller is chosen for the performance while the rest is used to extend the domain of attraction. The second one aims to improve the performance of the first one by combining model predictive control and interpolating control. The proposed approaches not only guarantee recursive feasibility and asymptotic stability, but also are optimal for state near the origin.