Model predictive control for continuous-time Markov Jump Linear Systems

This paper mainly studies the continuous-time Markov Jump Linear Systems (MJLSs) problem based on model predictive control (MPC). Sufficient conditions of the optimization problem, which could guarantee the mean square stability of the close-loop MJLS, are given at every sample time. Since the MPC strategy is aggregated into continuous-time MJLSs, a discrete-time controller is employed to deal with a continuous-time plant and the adopted cost function not only refers to the knowledge of system state but also considers the sampling period. In addition, the feasibility of MPC scheme and the mean square stability of the MJLS are deeply discussed by using the invariant ellipsoid. Finally, the main results are verified by a numerical example.