Summary: 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.
{© 2021 John Wiley & Sons Ltd.}