Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

doi:10.1109/JAS.2023.123843

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications
	Ding Wang; Ning Gao; Derong Liu; Jinna Li; Frank L. Lewis
刊名	IEEE/CAA Journal of Automatica Sinica
	2024
卷号	11 期号:1 页码:18-36
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)
ISSN号	2329-9266
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.
内容类型	期刊论文
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.