Robust receding horizon predictive control for systems with uncertain dynamics and input saturation

A receding horizon predictive control method which assures stability for systems with model uncertainty and input saturation is derived by extending earlier work in two important respects: (i) ellipsoidal invariant sets are replaced by polyhedral invariant sets; and (ii) the constraint that the state lie in an invariant set is invoked N steps ahead. The new algorithm allows for a considerable enlargement of the stabilizable set and provides extra degrees of freedom with which to improve performance.