Robust model predictive control for Markov jump systems subject to actuator saturation

A robust model predictive controller design method is proposed for a class of uncertain discrete-time Markov jump systems subject to actuator saturation.In terms of the engineering application,the uncertainties are considered to exist in system parameters and jumping transition probabilities,both of which are assumed to belong to some convex sets.The predictive controller is obtained by minimizing a worst-case infinite horizon objective function at each sampling time and the predictive control sequence is presented as a saturated state feedback control law at each step.The resulting closed-loop system is mean square stable and the proposed controller is obtained using semidefinite programming(SDP) which can be easily solved by means of linear matrix inequalities.The simulation results of a numerical example are given to verify the effectiveness of this method.