Robust Self‐Triggered Tube‐Based Model Predictive Control for Linear Systems: Integrated Design of Triggering and Control

ABSTRACT This article introduces a novel robust self‐triggered tube‐based model predictive control (TMPC) strategy for discrete‐time uncertain linear systems affected by bounded additive disturbances. Unlike traditional methods that treat control design and triggering as separate tasks, the proposed framework integrates the co‐design of control inputs and triggering intervals into a single optimization problem. This integration enables the simultaneous computation of the optimal control sequence and the maximum allowable triggering interval at each sampling instant, significantly reducing online computational complexity. Additionally, it introduces a hybrid control policy that transitions between open‐loop and closed‐loop control modes throughout the prediction horizon, effectively limiting uncertainty growth and ensuring feasibility over longer time frames. By incorporating the triggering interval directly into the cost function, the proposed method achieves a favorable balance between control performance and resource efficiency. Simulation results demonstrate the effectiveness of the approach in reducing communication frequency and computational load, while ensuring robust stability and the satisfaction of constraints.