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| A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification | |
Wang, Ding1,2 ; Mu, Chaoxu3
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| 发表期刊 | NEUROCOMPUTING
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| 2017-11-29 | |
| 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 |
| DOI | 10.1016/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 | 多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队 |
| 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 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 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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