Robust Self-Triggered Min-Max Model Predictive Control for Linear Discrete-Time Systems

摘要

In this paper, we propose a self-triggered robust model predictive control (MPC) algorithm for constrained linear discrete-time systems subject to additive disturbances. First, worst case scenarios are considered in the MPC problem formulation to achieve robust constraint satisfaction. Second, a self-triggered control scheduler is proposed to minimize the frequency of performing optimization and the closed-loop system is shown to be recursive feasible and input-to-state practical stable in its region of attraction. Finally, a numerical example demonstrates the effectiveness of the proposed control strategy.