Output feedback predictive control for constrained linear systems with intermittent measurements

This paper studies the robust output feedback model predictive control (MPC) problem for a constrained linear system subject to periodical measurement losses and external disturbances. The overall robust output feedback model predictive controller consists of a robust observer that can accommodate the lost measurement and a new state feedback model predictive controller fulfilling the input and state constraints. Based on the designed observer, the error bounds of the system state estimate are established. By incorporating the estimation error bounds and the external disturbances, the input and state constraints are augmented and further tightened for the new state feedback model predictive controller. Furthermore, the iterative feasibility of the proposed robust output feedback MPC algorithm is proved. It is shown that the closed-loop system is asymptotically stable and the system state will periodically converge to several compact sets. Finally, simulation results and comparison studies are provided to verify effectiveness of the proposed robust output feedback MPC algorithm.