Robust receding horizon control of discrete-time Markovian jump uncertain systems
This paper proposes a receding horizon control scheme for a set of uncertain discrete-time linear systems with randomly jumping parameters described by a finite-state Markov process whose jumping transition probabilities are assumed to belong to some convex sets. The control scheme for the underlying systems is based on the minimization of an upper bound on the worst-case infinite horizon cost function at each time instant. It is shown that the mean square stability of the proposed control system is guaranteed under some matrix inequality conditions on the terminal weighting matrices. The proposed controller is obtained using semidefinite programming.
receding horizon control markovian jump uncertain systems semidefinite programming robust control