Adaptive Critic Learning for Constrained Optimal Event-Triggered Control With Discounted Cost

doi:10.1109/TNNLS.2020.2976787

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	Adaptive Critic Learning for Constrained Optimal Event-Triggered Control With Discounted Cost
	Yang, Xiong 1; Wei, Qinglai 2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021
卷号	32 期号:1 页码:91-104
关键词	Nonlinear systems Optimal control Robustness Cost function Adaptive systems Adaptive critic designs (ACDs) adaptive critic learning (ACL) adaptive dynamic programming (ADP) constrained optimal control event-triggered control (ETC) reinforcement learning (RL)
ISSN号	2162-237X
DOI	10.1109/TNNLS.2020.2976787
通讯作者	Yang, Xiong(xiong.yang@tju.edu.cn)
英文摘要	This article studies an optimal event-triggered control (ETC) problem of nonlinear continuous-time systems subject to asymmetric control constraints. The present nonlinear plant differs from many studied systems in that its equilibrium point is nonzero. First, we introduce a discounted cost for such a system in order to obtain the optimal ETC without making coordinate transformations. Then, we present an event-triggered Hamilton-Jacobi-Bellman equation (ET-HJBE) arising in the discounted-cost constrained optimal ETC problem. After that, we propose an event-triggering condition guaranteeing a positive lower bound for the minimal intersample time. To solve the ET-HJBE, we construct a critic network under the framework of adaptive critic learning. The critic network weight vector is tuned through a modified gradient descent method, which simultaneously uses historical and instantaneous state data. By employing the Lyapunov method, we prove that the uniform ultimate boundedness of all signals in the closed-loop system is guaranteed. Finally, we provide simulations of a pendulum system and an oscillator system to validate the obtained optimal ETC strategy.
资助项目	National Natural Science Foundation of China[61973228] ; National Natural Science Foundation of China[61722312]
WOS关键词	UNCERTAIN NONLINEAR-SYSTEMS ; OPTIMAL TRACKING CONTROL ; ROBUST-CONTROL ; TIME-SYSTEMS
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000641162100008
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44681]
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	Yang, Xiong
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Xiong,Wei, Qinglai. Adaptive Critic Learning for Constrained Optimal Event-Triggered Control With Discounted Cost[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(1):91-104.
APA	Yang, Xiong,&Wei, Qinglai.(2021).Adaptive Critic Learning for Constrained Optimal Event-Triggered Control With Discounted Cost.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(1),91-104.
MLA	Yang, Xiong,et al."Adaptive Critic Learning for Constrained Optimal Event-Triggered Control With Discounted Cost".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.1(2021):91-104.