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

作     者：Weinan Gao Zhong-Ping Jiang Weinan Gao;Zhong-Ping Jiang

作者机构：Department of Mechanical and Civil EngineeringCollege of Engineering and Science Florida Institute of Technology150 W.University Blvd.MelbourneFL32901USA Department of Electrical and Computer EngineeringTandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA 

出 版 物：《Control Theory and Technology》 (控制理论与技术（英文版）)

年 卷 期：2022年第20卷第1期

页      面：1-19页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：the U.S.National Science Foundation(EPCN-1903781 CMMI-2138206) 

主　　题：Adaptive optimal output regulation Adaptive dynamic programming Reinforcement learning Learning-based control 

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 *** 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 *** this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven *** will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.