| 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. |
| 关键词: Adaptive optimal output regulation · Adaptive dynamic programming · Reinforcement learning · Learning-based control |
| DOI：https://doi.org/10.1007/s11768-022-00081-3 |
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| 基金项目:This work was supported in part by the U.S. National Science Foundation (EPCN-1903781, CMMI-2138206) |
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| Learning-based adaptive optimal output regulation of linear and nonlinear systems: An overview |
| Weinan Gao1,Zhong-Ping Jiang2 |
| (1 Florida Institute of Technology;2 New York University) |
| Abstract: |
| 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. |
| Key words: Adaptive optimal output regulation · Adaptive dynamic programming · Reinforcement learning · Learning-based control |
