An observer-based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

The present paper addresses an observer-based output feedback robust model predictive control for the linear parameter varying system with bounded disturbance and noise subject to input and state constraints. The main contribution is that the on-line convex optimization problem not only simultaneously optimizes the observer and controller gains to stabilize the augmented closed-loop system but also incorporates the refreshment of bounds of the estimation error set. The optimization problem steers the nominal augmented closed-loop system to converge to the origin, and the real augmented closed-loop system bounded within robust positive invariant set converges to a neighborhood of the origin such that recursive feasibility of the optimization and robust stability of the controlled system are ensured. Two numerical examples are given to illustrate the effectiveness of the method.