Finite‐time self‐triggered model predictive control of discrete‐time Markov jump linear systems

Abstract In the present study, a self‐triggered model predictive control (MPC) strategy is proposed for a class of discrete‐time Markov jump linear systems (MJLSs) to achieve the desired control performance in a finite‐time interval and simultaneously save the computational resources. Obtained results show that with eminent optimization performance and low computational complexity of tube‐based MPC algorithm, it guarantees stochastic finite‐time boundedness of MJLSs. Meanwhile, a self‐triggered scheme is proposed to reduce unnecessary sampling when the system state satisfies the control target. Furthermore, the cost function of the MPC algorithm and the error‐based self‐triggered scheme are adjusted to keep the state trajectories within prespecified bounds over a given time interval. Finally, the effectiveness of the proposed strategy is numerically evaluated from different aspects, including the overall performance and resource‐saving capability.