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A partial policy iteration ADP algorithm for nonlinear neuro-optimal control with discounted total reward

文献类型：期刊论文

作者Liang, Mingming1,2; Wei, Qinglai1
刊名NEUROCOMPUTING
出版日期2021-02-01
卷号424页码:23-34
关键词Adaptive critic designs Adaptive dynamic programming Policy iteration Neural networks Neuro-dynamic programming Nonlinear systems Optimal control
ISSN号0925-2312
DOI10.1016/j.neucom.2020.11.014
通讯作者Liang, Mingming(liangmingming@gdut.edu.cn)
英文摘要This paper constructs a partial policy iteration adaptive dynamic programming (ADP) algorithm to solve the optimal control problem of nonlinear systems with discounted total reward. Compared with traditional policy iteration ADP algorithm, the approach updates the iterative control law only in a local region of the global system state space. With the benefit of this feature, the overall computational burden at each iteration for processing units can be significantly reduced. Hence, this feature enables our algorithm to be successfully executed on low-performance devices such as smartphones, smartwatches and the Internet of Things (IoT) objects. We provide the convergency analysis to show that the generated sequence of value functions is monotonically nonincreasing and can finally reach a local optimum. In addition, the corresponding local policy space is developed theoretically for the first time. Besides, when the sequence of the local system state spaces is chosen properly, we prove that the developed algorithm is capable of finding the global optimal performance index function for the nonlinear systems. Finally, we present a numerical simulation to demonstrate the effectiveness of the proposed algorithm. (c) 2020 Elsevier B.V. All rights reserved.
WOS关键词LINEAR-SYSTEMS ; ROBUST-CONTROL ; GAMES
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000611084200003
出版者ELSEVIER
源URL[http://ir.ia.ac.cn/handle/173211/43115]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
通讯作者Liang, Mingming
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
推荐引用方式
GB/T 7714		 Liang, Mingming,Wei, Qinglai. A partial policy iteration ADP algorithm for nonlinear neuro-optimal control with discounted total reward[J]. NEUROCOMPUTING,2021,424:23-34.
APA Liang, Mingming,&Wei, Qinglai.(2021).A partial policy iteration ADP algorithm for nonlinear neuro-optimal control with discounted total reward.NEUROCOMPUTING,424,23-34.
MLA Liang, Mingming,et al."A partial policy iteration ADP algorithm for nonlinear neuro-optimal control with discounted total reward".NEUROCOMPUTING 424(2021):23-34.

入库方式： OAI收割

来源：自动化研究所

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