Constrained linear output regulation via measurement output feedback model predictive control

Abstract

Recently, the output regulation problem with input constraints for incrementally stable plants was solved by an error feedback model predictive control (MPC) algorithm. This paper further considers the case with both state and input constraints for linear systems and uses a measurement output feedback MPC algorithm to solve the problem. By relaxing two restrictions, the result of this paper can handle a larger class of linear systems than the existing result. Due to the uncontrollability of the augmented system composed of the plant and the exosystem, a key assumption in the literature fails, we manage to overcome this difficulty by introducing novel tightened constraints and the terminal constraint. As a result, the proposed algorithm solves the linear output regulation problem in the presence of both state and input constraints. The effectiveness of our design is illustrated by a numerical example.