Model Predictive Control of Markov Jump Linear Systems: an Error-based Self-triggered approach

This article proposes a self-triggered model predictive control algorithm for discrete-time Markov jump linear systems to fulfill computational resources saving goals. Specifically, a self-triggered scheme is presented for computing the next sampling time based on the error between the current sampled and the predicted information. The feasibility of online controller design is proved under this aperiodic sampling. Finally, a numerical example verifies the effectiveness and the resource-saving capacity of the developed approach.