A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification
Wang, Ding1,2; Mu, Chaoxu3
2017-11-29
发表期刊NEUROCOMPUTING
卷号266页码:353-360
文章类型Article
摘要In 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.
关键词Adaptive Dynamic Programming Adaptive System Learning Control Neural Network Optimal Regulator Stability
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2017.05.051
关键词[WOS]SYSTEMS
收录类别SCI
语种英语
项目资助者National 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研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000408183900033
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20716
专题复杂系统管理与控制国家重点实验室_平行控制
作者单位1.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
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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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