Knowledge Commons of Institute of Automation，CAS
| Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors | |
Wei, Qinglai ; Liu, Derong; Qinglai Wei
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| 发表期刊 | NEUROCOMPUTING
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| 2015-02-03 | |
| 卷号 | 149期号:x页码:106-115 |
| 文章类型 | Article |
| 摘要 | In this paper, a new infinite horizon neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems is developed. The idea is to use iterative adaptive dynamic programming (ADP) algorithm to obtain the iterative tracking control law which makes the iterative performance index function reach the optimum. When the iterative tracking control law and iterative performance index function in each iteration cannot be accurately obtained, the convergence criteria of the iterative ADP algorithm are established according to the properties with finite approximation errors. If the convergence conditions are satisfied, it shows that the iterative performance index functions can converge to a finite neighborhood of the lowest bound of all performance index functions. Properties of the finite approximation errors for the iterative ADP algorithm are also analyzed. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Convergence properties of the neural network weights are proven. Finally, simulation results are given to illustrate the performance of the developed method. (C) 2014 Elsevier B.V. All rights reserved. |
| 关键词 | Adaptive Dynamic Programming Adaptive Critic Designs Approximate Dynamic Programming Value Iteration Approximation Errors Optimal Tracking Control |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.neucom.2013.09.069 |
| 关键词[WOS] | DYNAMIC-PROGRAMMING ALGORITHM ; ONLINE LEARNING CONTROL ; CRITIC DESIGNS ; REINFORCEMENT ; CONVERGENCE ; ITERATION ; PROOF |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000360028800015 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/8958 |
| 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 |
| 通讯作者 | Qinglai Wei |
| 作者单位 | Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, 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 | Wei, Qinglai,Liu, Derong,Qinglai Wei. Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors[J]. NEUROCOMPUTING,2015,149(x):106-115. |
| APA | Wei, Qinglai,Liu, Derong,&Qinglai Wei.(2015).Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors.NEUROCOMPUTING,149(x),106-115. |
| MLA | Wei, Qinglai,et al."Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors".NEUROCOMPUTING 149.x(2015):106-115. |
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| 2015_Neurocomputing_（836KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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