Output feedback robust MPC for linear systems with norm-bounded model uncertainty and disturbance

Abstract This paper presents an output feedback robust model predictive control strategy for linear systems with norm-bounded model parametric uncertainty and disturbance. In order to reduce the computational burden and enhance the control performance, in the optimization problem we optimize the estimator state matrix instead of the state estimator gain. The infinite horizon control moves and estimated states are parameterized by a dynamic output feedback control law, where its state feedback gain and estimator state matrix are optimized at each sampling instant. By appropriately refreshing the estimation error set, the optimization problem is shown to be recursively feasible and the convergence of the augmented state is guaranteed. A numerical example is given to show the effectiveness of the proposed approach.