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

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	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, Qinglai 2,3,4; Lu, Jingwei 2,4
刊名	NEUROCOMPUTING
	2022-04-28
卷号	483 页码:361-369
关键词	Integral reinforcement learning Nuclear power reactor Nonlinear system Optimal tracking control Neural networks
ISSN号	0925-2312
DOI	10.1016/j.neucom.2022.01.034
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn)
英文摘要	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.
WOS关键词	TIME LINEAR-SYSTEMS ; PARALLEL CONTROL ; POWER ; REACTOR ; DESIGN
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000776152200001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48292]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
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.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
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.