Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input

doi:10.1109/TNNLS.2018.2844165

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	Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input
	Liu, Yan-Jun1 ; Li, Shu 1; Tong, Shaocheng 1; Chen, C. L. Philip 2,3,4
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2019
卷号	30 期号:1 页码:295-305
通讯作者	Liu, Yan-Jun(liuyanjun@live.com)
摘要	In this paper, an optimal control algorithm is designed for uncertain nonlinear systems in discrete-time, which are in nonaffine form and with unknown dead-zone. The main contributions of this paper are that an optimal control algorithm is for the first time framed in this paper for nonlinear systems with nonaffine dead-zone, and the adaptive parameter law for dead-zone is calculated by using the gradient rules. The mean value theory is employed to deal with the nonaffine dead-zone input and the implicit function theory based on reinforcement learning is appropriately introduced to find an unknown ideal controller which is approximated by using the action network. Other neural networks are taken as the critic networks to approximate the strategic utility functions. Based on the Lyapunov stability analysis theory, we can prove the stability of systems, i.e., the optimal control laws can guarantee that all the signals in the closed-loop system are bounded and the tracking errors are converged to a small compact set. Finally, two simulation examples demonstrate the effectiveness of the design algorithm.
关键词	Discrete-time systems neural networks (NNs) nonlinear systems optimal control reinforcement learning
DOI	10.1109/TNNLS.2018.2844165
关键词[WOS]	BARRIER LYAPUNOV FUNCTIONS ; OUTPUT-FEEDBACK CONTROL ; DYNAMIC SURFACE CONTROL ; TRACKING CONTROL ; MULTIAGENT SYSTEMS ; POLICY ITERATION ; NETWORK CONTROL ; DESIGN ; COMPENSATION ; ALGORITHM
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61622303] ; National Natural Science Foundation of China[61603164] ; National Natural Science Foundation of China[61473139] ; National Natural Science Foundation of China[61773188] ; Program for Liaoning Innovative Research Team in University[LT2016006] ; Program for Distinguished Professor of Liaoning Province ; National Natural Science Foundation of China[61622303] ; National Natural Science Foundation of China[61603164] ; National Natural Science Foundation of China[61473139] ; National Natural Science Foundation of China[61773188] ; Program for Liaoning Innovative Research Team in University[LT2016006] ; Program for Distinguished Professor of Liaoning Province
项目资助者	National Natural Science Foundation of China ; Program for Liaoning Innovative Research Team in University ; Program for Distinguished Professor of Liaoning Province
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:000454329300024
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：78[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/173211/25628
专题	离退休人员
通讯作者	Liu, Yan-Jun
作者单位	1.Liaoning Univ Technol, Coll Sci, Jinzhou 121001, Peoples R China 2.Univ Macau, Dept Comp & Informat Sci, Fac Sci & Technol, Macau 99999, Peoples R China 3.Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yan-Jun,Li, Shu,Tong, Shaocheng,et al. Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(1):295-305.
APA	Liu, Yan-Jun,Li, Shu,Tong, Shaocheng,&Chen, C. L. Philip.(2019).Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(1),295-305.
MLA	Liu, Yan-Jun,et al."Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.1(2019):295-305.