Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview
Weinan Gao1
Zhong-Ping Jiang2
1.Department of Mechanical and Civil Engineering,College of Engineering and Science Florida Institute of Technology,150 W.University Blvd.,Melbourne,FL 32901,USA2.Department of Electrical and Computer Engineering,Tandon School of Engineering,New York University,Six MetroTech Center,Brooklyn,NY 11201,USA
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.
机标关键词：
论文发表日期：2022-02-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：19( 1-19 )
英文信息