Adaptive optimal output regulation of linear discrete-time systems based on event-triggered output-feedback

被引：32

作者：

Zhao, Fuyu ^{[1
]}

Gao, Weinan ^{[2
]}

Liu, Tengfei ^{[1
]}

Jiang, Zhong-Ping ^{[3
]}

机构：

[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China

[2] Florida Inst Technol, Coll Engn & Sci, Dept Mech & Civil Engn, Melbourne, FL 32901 USA

[3] NYU, 6 Metrotech Ctr, Dept Elect & Comp Engn, Brooklyn, NY 11201 USA

来源：

AUTOMATICA | 2022年 / 137卷

基金：

美国国家科学基金会; 日本学术振兴会; 中国国家自然科学基金; 澳大利亚研究理事会;

关键词：

Adaptive dynamic programming; Output regulation; Event-triggered control; OPTIMAL TRACKING CONTROL; NONLINEAR-SYSTEMS; STABILITY; ERROR; MODEL; GAIN;

D O I：

10.1016/j.automatica.2021.110103

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents novel event-triggered control approaches to solve the adaptive optimal output regulation problem for a class of linear discrete-time systems. Different from most existing research on output regulation problems, the developed adaptive optimal control approaches are based on (1) output-feedback instead of full-state or partial-state feedback, (2) adaptive dynamic programming (ADP) which provides approximate solutions of the optimal control problem without requiring the precise knowledge of the plant dynamics, and (3) an event-triggering mechanism that reduces the communication between the controller and the plant. It is shown that the system in closed-loop with the developed controllers is asymptotically stable at an equilibrium of interest, and the tracking errors asymptotically converge to zero. Moreover, the suboptimality of the closed-loop system is directly determined by the relative threshold, which is a ratio between the triggering threshold and the actual state. A numerical simulation example is employed to verify the effectiveness of the proposed methodologies. (C) 2021 Elsevier Ltd. All rights reserved.

引用

页数：10

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