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

doi:10.1109/TCYB.2022.3164977

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

	Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control
	Lu, Jingwei 2,3; Wei, Qinglai 2,3; Zhou, Tianmin 2,3; Wang, Ziyang 1; Wang, Fei-Yue 2,3
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2022-05-06
页码	15
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
ISSN号	2168-2267
DOI	10.1109/TCYB.2022.3164977
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要	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.
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)
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000795212400001
资助机构	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)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49383]
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	Wang, Fei-Yue
作者单位	1.Tsinghua Univ, Dept Automat, Beijing 100084, 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
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.