A New Neuro-Optimal Nonlinear Tracking Control Method via Integral Reinforcement Learning with Applications to Nuclear Systems

doi:10.1016/j.neucom.2022.01.034

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	A New Neuro-Optimal Nonlinear Tracking Control Method via Integral Reinforcement Learning with Applications to Nuclear Systems
	Zhong, Weifeng 1; Wang, Mengxuan 1,2; Wei, Qinglai2,3,4 ; Lu, Jingwei2,3
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2022-04-28
卷号	483 页码:361-369
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn)
摘要	In this paper, a new infinite horizon optimal tracking control method for continuous-time nonlinear sys-tems is given using an actor-critic structure. This present integral reinforcement learning (IRL) method is a novelty method in adaptive dynamic programming (ADP) algorithms and an online policy iteration algorithm. For the optimal tracking problem, the cost function is defined by tracking errors. Consequently, the goal is to minimize tracking errors toward desired trajectories. Since it is hard to solve the Hamilton-Jacobi-Bellman (HJB) equation for continuous-time nonlinear systems control problems, leveraging the actor-critic architecture with neural networks (NNs) to approximate the tracking error performance index and error control law is necessary. Instead of using conventional neural networks, we employ higher-order polynomials in the whole actor-critic architecture. Finally, we apply this new neuro-optimal tracking method to the 2500MW pressurized water reactor (PWR) nuclear power plant, and simulation results are given to demonstrate the effectiveness of the developed method.(c) 2022 Published by Elsevier B.V.
关键词	Integral reinforcement learning Nuclear power reactor Nonlinear system Optimal tracking control Neural networks
DOI	10.1016/j.neucom.2022.01.034
关键词[WOS]	TIME LINEAR-SYSTEMS ; PARALLEL CONTROL ; POWER ; REACTOR ; DESIGN
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000776152200001
出版者	ELSEVIER
七大方向——子方向分类	智能控制
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48293
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Wei, Qinglai
作者单位	1.Harbin Univ Sci & Technol, Harbin, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhong, Weifeng,Wang, Mengxuan,Wei, Qinglai,et al. A New Neuro-Optimal Nonlinear Tracking Control Method via Integral Reinforcement Learning with Applications to Nuclear Systems[J]. NEUROCOMPUTING,2022,483:361-369.
APA	Zhong, Weifeng,Wang, Mengxuan,Wei, Qinglai,&Lu, Jingwei.(2022).A New Neuro-Optimal Nonlinear Tracking Control Method via Integral Reinforcement Learning with Applications to Nuclear Systems.NEUROCOMPUTING,483,361-369.
MLA	Zhong, Weifeng,et al."A New Neuro-Optimal Nonlinear Tracking Control Method via Integral Reinforcement Learning with Applications to Nuclear Systems".NEUROCOMPUTING 483(2022):361-369.