Fast Self-Triggered MPC for Constrained Linear Systems With Additive Disturbances

This article proposes a robust self-triggered model predictive control (MPC) algorithm for class of constrained linear systems subject to bounded additive disturbances, in which the intersampling time is determined by fast convergence mechanism. The main idea mechanism select sampling interval so that rapid decrease predicted costs associated with optimal inputs guaranteed. allows reduction required computation without compromising performance. By using constraint tightening technique and exploring nature open-loop between instants, set minimally conservative constraints imposed on nominal states ensure satisfaction. A multistep MPC optimization problem formulated, ensures recursive feasibility all possible realizations disturbance. closed-loop system guaranteed satisfy mean-square stability condition. To further reduce computational load, when reach predetermined neighborhood origin, law switches local controller. compact state space shown be robustly asymptotically stabilized. Numerical comparisons are provided demonstrate effectiveness proposed strategies.