Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview

被引：17
作者：
Gao, Weinan [1 ]
Jiang, Zhong-Ping [2 ]
机构：
[1] Florida Inst Technol, Coll Engn & Sci, Dept Mech & Civil Engn, 150 W Univ Blvd, Melbourne, FL 32901 USA
[2] NYU, Tandon Sch Engn, Dept Elect & Comp Engn, 6 MetroTech Ctr, Brooklyn, NY 11201 USA
来源：
CONTROL THEORY AND TECHNOLOGY | 2022年 / 20卷 / 01期
基金：
美国国家科学基金会;
关键词：
Adaptive optimal output regulation; Adaptive dynamic programming; Reinforcement learning; Learning-based control; INTERNAL-MODEL PRINCIPLE; MULTIAGENT SYSTEMS; NEURAL-NETWORKS; VALUE-ITERATION; CRUISE CONTROL; STABILITY; CONTROLLERS; TRACKING; GAME; GO;
D O I：
10.1007/s11768-022-00081-3
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 reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection. The proposed framework aims to bring together two separate topics-output regulation and adaptive dynamic programming-that have been under extensive investigation due to their broad applications in modern control engineering. Under this framework, one can solve optimal output regulation problems of linear, partially linear, nonlinear, and multi-agent systems in a data-driven manner. We will also review some practical applications based on this framework, such as semi-autonomous vehicles, connected and autonomous vehicles, and nonlinear oscillators.
引用
收藏
页码：1 / 19
页数：19
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