Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control

doi:10.1109/TCYB.2022.3164977

CASIA OpenIR > 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

	Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control
	Lu, Jingwei1,2 ; Wei, Qinglai1,2 ; Zhou, Tianmin 1,2; Wang, Ziyang 3; Wang, Fei-Yue1,2
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2022-05-06
页码	15
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
摘要	This article uses parallel control to investigate the problem of event-triggered near-optimal control (ETNOC) for unknown discrete-time (DT) nonlinear systems. First, to achieve parallel control, an augmented nonlinear system (ANS) with an augmented performance index (API) is proposed to introduce the control input into the feedback system. The control stability relationship between the ANS and the original system is analyzed, and it is shown that, by choosing a proper API, optimal control of the ANS with the API can be seen as near-optimal control of the original system with the original performance index (OPI). Second, based on parallel control, a novel event-triggered scheme is proposed, and then a novel ETNOC method is developed using the time-triggered optimal value function of the ANS with the API. The control stability is proved, and an upper bound, which is related to the design parameter, is provided for the actual performance index in advance. Then, to implement the developed ETNOC method for unknown DT nonlinear systems, a novel online learning algorithm is developed without reconstructing unknown systems, and neural network (NN) and adaptive dynamic programming (ADP) techniques are employed in the developed algorithm. The convergence of the signals in the closed-loop system (CLS) is shown using the Lyapunov approach, and the assumption of boundedness of input dynamics is not required. Finally, two simulations justify the theoretical conjectures.
关键词	Nonlinear systems Optimal control Control systems Performance analysis Stability criteria Iterative algorithms Heuristic algorithms Adaptive dynamic programming (ADP) event-triggered control near-optimal control parallel control reinforcement learning (RL) unknown nonlinear systems
DOI	10.1109/TCYB.2022.3164977
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0101502] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[62073321] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRIIACV)
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRIIACV)
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000795212400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：16[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/173211/49384
专题	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
通讯作者	Wang, Fei-Yue
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
第一作者单位	中国科学院自动化研究所
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	Lu, Jingwei,Wei, Qinglai,Zhou, Tianmin,et al. Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022:15.
APA	Lu, Jingwei,Wei, Qinglai,Zhou, Tianmin,Wang, Ziyang,&Wang, Fei-Yue.(2022).Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control.IEEE TRANSACTIONS ON CYBERNETICS,15.
MLA	Lu, Jingwei,et al."Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control".IEEE TRANSACTIONS ON CYBERNETICS (2022):15.