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A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification
Wang, Ding1,2; Mu, Chaoxu3
Source PublicationNEUROCOMPUTING
2017-11-29
Volume266Pages:353-360
SubtypeArticle
AbstractIn this paper, we focus on developing adaptive optimal regulators for a class of continuous-time nonlinear dynamical systems through an improved neural learning mechanism. The main objective lies in that establishing an additional stabilizing term to reinforce the traditional training process of the critic neural network, so that to reduce the requirement with respect to the initial stabilizing control, and therefore, bring in an obvious convenience to the adaptive-critic-based learning control implementation. It is exhibited that by employing the novel updating rule, the adaptive optimal control law can be obtained with an excellent approximation property. The closed-loop system is constructed and its stability issue is handled by considering the improved learning criterion. Experimental simulations are also conducted to verify the efficient performance of the present design method, especially the major role that the stabilizing term performed. (C) 2017 Elsevier B.V. All rights reserved.
KeywordAdaptive Dynamic Programming Adaptive System Learning Control Neural Network Optimal Regulator Stability
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2017.05.051
WOS KeywordSYSTEMS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(U1501251 ; Beijing Natural Science Foundation(4162065) ; Research Fund of Tianjin Key Laboratory of Process Measurement and Control(TKLPMC-201612) ; Early Career Development Award of SKLMCCS ; 61533017 ; 61233001)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000408183900033
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20716
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Tianjin Univ, Sch Elect & Informat Engn, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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GB/T 7714
Wang, Ding,Mu, Chaoxu. A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification[J]. NEUROCOMPUTING,2017,266:353-360.
APA Wang, Ding,&Mu, Chaoxu.(2017).A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification.NEUROCOMPUTING,266,353-360.
MLA Wang, Ding,et al."A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification".NEUROCOMPUTING 266(2017):353-360.
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