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

doi:10.1109/TNNLS.2020.2976787

CASIA OpenIR > 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

	Adaptive Critic Learning for Constrained Optimal Event-Triggered Control With Discounted Cost
	Yang, Xiong1 ; Wei, Qinglai2
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021
卷号	32 期号:1 页码:91-104
通讯作者	Yang, Xiong(xiong.yang@tju.edu.cn)
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.
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)
DOI	10.1109/TNNLS.2020.2976787
关键词[WOS]	UNCERTAIN NONLINEAR-SYSTEMS ; OPTIMAL TRACKING CONTROL ; ROBUST-CONTROL ; TIME-SYSTEMS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61973228] ; National Natural Science Foundation of China[61722312]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000641162100008
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：52[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/173211/44682
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
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.