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

• 成果类型: Article
• 署名作者: Dai, Li; Cannon, Mark; Yang, Fuwen; Yan, Shuhao
• 署名单位: Beijing Institute of Technology; University of Oxford; Griffith University; Griffith University - Gold Coast Campus
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3022734
• 发表日期: 2021
• 页码: 3624-3637
• 关键词: robustness; optimization; Heuristic algorithms; CONVERGENCE; Additives; Prediction algorithms; control systems; Fast convergence; model predictive control (MPC); Robustness; self-triggered control

摘要

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