CASIA OpenIR
Optimized Adaptive Nonlinear Tracking Control Using Actor-Critic Reinforcement Learning Strategy
Wen, Guoxing1,2; Chen, C. L. Philip3,4,5; Ge, Shuzhi Sam6,7; Yang, Hongli8; Liu, Xiaoguang9,10
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
2019-09-01
Volume15Issue:9Pages:4969-4977
Corresponding AuthorWen, Guoxing(wengx_sd@hotmail.com)
AbstractThis paper proposes an optimized tracking control approach using neural network (NN) based reinforcement learning (RL) for a class of nonlinear dynamic systems, which requires both tracking and optimizing to be performed simultaneously. Generally, for obtaining optimal control solution, Hamilton-Jacobi-Bellman equation is expected to be solvable, but, owing to strong nonlinearity, the equation is solved difficultly or even impossibly by analytical methods. Therefore, adaptive NN approximation based RL is usually considered. In the optimized control design, for driving output state following to the desired trajectory, an error term is split from optimal performance index function, and then both actor and critic NNs are built to perform RL algorithm. Actor NN aims to execute control behaviors, and critic NN aims to appraise control performance and make feedback to actor. The proof of stability concludes that the desired control performances are obtained. A numerical simulation is designed and implemented, and the desired results are shown.
KeywordLyapunov function neural networks (NNs) nonlinear systems optimized tracking control reinforcement learning (RL) of actor-critic architecture
DOI10.1109/TII.2019.2894282
WOS KeywordNEURAL-NETWORKS ; SYSTEMS
Indexed BySCI
Language英语
Funding ProjectShandong Provincial Natural Science Foundation, China[ZR2018MF015] ; National Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61572540] ; Doctoral Scientific Research Staring Fund of Binzhou University[2016Y14] ; mobility program of Shandong University of Science and Technology
Funding OrganizationShandong Provincial Natural Science Foundation, China ; National Natural Science Foundation of China ; Doctoral Scientific Research Staring Fund of Binzhou University ; mobility program of Shandong University of Science and Technology
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS IDWOS:000489584600012
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26670
Collection中国科学院自动化研究所
Corresponding AuthorWen, Guoxing
Affiliation1.Binzhou Univ, Coll Sci, Binzhou 256600, Peoples R China
2.Binzhou Univ, IAET, Binzhou 256600, Peoples R China
3.Univ Macau, Dept Comp & Informat Sci, Fac Sci & Technol, Macau 99999, Peoples R China
4.Dalian Maritime Univ, Dalian 116026, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
6.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
7.Qingdao Univ, Inst Future, Qingdao 266071, Shandong, Peoples R China
8.Shandong Univ Sci & Technol, Math & Syst Sci Coll, Qingdao 266590, Shandong, Peoples R China
9.Southwest Minzu Univ, Key Lab Comp Syst, State Ethn Affairs Commiss, Chengdu 610041, Sichuan, Peoples R China
10.Southwest Minzu Univ, Sch Comp Sci & Technol, Chengdu 610041, Sichuan, Peoples R China
Recommended Citation
GB/T 7714
Wen, Guoxing,Chen, C. L. Philip,Ge, Shuzhi Sam,et al. Optimized Adaptive Nonlinear Tracking Control Using Actor-Critic Reinforcement Learning Strategy[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2019,15(9):4969-4977.
APA Wen, Guoxing,Chen, C. L. Philip,Ge, Shuzhi Sam,Yang, Hongli,&Liu, Xiaoguang.(2019).Optimized Adaptive Nonlinear Tracking Control Using Actor-Critic Reinforcement Learning Strategy.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,15(9),4969-4977.
MLA Wen, Guoxing,et al."Optimized Adaptive Nonlinear Tracking Control Using Actor-Critic Reinforcement Learning Strategy".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 15.9(2019):4969-4977.
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