Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications
文献类型：期刊论文
| 作者 | Ding Wang ; Ning Gao; Derong Liu; Jinna Li; Frank L. Lewis
|
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2024 |
| 卷号 | 11期号:1页码:18-36 |
| ISSN号 | 2329-9266 |
| 关键词 | Adaptive dynamic programming (ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning (RL) |
| DOI | 10.1109/JAS.2023.123843 |
| 英文摘要 | Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54491]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Ding Wang,Ning Gao,Derong Liu,et al. Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(1):18-36. |
| APA | Ding Wang,Ning Gao,Derong Liu,Jinna Li,&Frank L. Lewis.(2024).Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications.IEEE/CAA Journal of Automatica Sinica,11(1),18-36. |
| MLA | Ding Wang,et al."Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications".IEEE/CAA Journal of Automatica Sinica 11.1(2024):18-36. |
入库方式： OAI收割
来源：自动化研究所
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