Knowledge Commons of Institute of Automation,CAS
A New Neuro-Optimal Nonlinear Tracking Control Method via Integral Reinforcement Learning with Applications to Nuclear Systems | |
Zhong, Weifeng1; Wang, Mengxuan1,2; 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 | |
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. |
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