CASIA OpenIR
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input
Liu, Yan-Jun1; Li, Shu1; Tong, Shaocheng1; Chen, C. L. Philip2,3,4
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2019
Volume30Issue:1Pages:295-305
Corresponding AuthorLiu, Yan-Jun(liuyanjun@live.com)
AbstractIn 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.
KeywordDiscrete-time systems neural networks (NNs) nonlinear systems optimal control reinforcement learning
DOI10.1109/TNNLS.2018.2844165
WOS KeywordBARRIER LYAPUNOV FUNCTIONS ; OUTPUT-FEEDBACK CONTROL ; DYNAMIC SURFACE CONTROL ; TRACKING CONTROL ; MULTIAGENT SYSTEMS ; POLICY ITERATION ; NETWORK CONTROL ; DESIGN ; COMPENSATION ; ALGORITHM
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational Natural Science Foundation of China ; Program for Liaoning Innovative Research Team in University ; Program for Distinguished Professor of Liaoning Province
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000454329300024
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25628
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Yan-Jun
Affiliation1.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
Recommended Citation
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.
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