Enhancing output feedback MPC for linear discrete-time systems with set-valued moving horizon estimation

Abstract

We propose a novel output feedback model predictive control scheme for linear discrete-time systems incorporating a set-valued estimator based on a fixed finite number of recent measurements. Recursive feasibility is established by basing predictions that are farther in the future on fewer measurements. The resulting optimization problem is convex with linear constraints. We demonstrate in a numerical example that the proposed model predictive control scheme allows an enlargement of the feasible set beyond what is possible with earlier schemes using linear estimators.