Self-Triggered Stochastic MPC for Linear Systems With Disturbances

In this letter, we present a self-triggering mechanism for stochastic model predictive control (SMPC) of discrete-time linear systems subject to probabilistic constraints, where the controller and the plant are connected by a shared communication network. The proposed triggering mechanism requires that only one control input is allowed to be transmitted through the network at each triggering instant which is then applied to the plant for several steps afterward. By doing so, communication is effectively reduced both in terms of frequency and total amount. We establish the theoretical result for recursive feasibility in the light of proper reformulation of constraints on the nominal system trajectories, and also provide stability analysis for the proposed self-triggered SMPC. A numerical example illustrates the efficiency of the proposed scheme in reducing the communication as well as ensuring meeting the probabilistic constraints.