Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm
文献类型：期刊论文
| 作者 | Wei, Qinglai1 ; Song, Ruizhuo2; Sun, Qiuye3; Qinglai Wei
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2015-11-30 |
| 卷号 | 168页码:520-528 |
| 关键词 | Adaptive dynamic programming Approximate Dynamic programming Q-learning Optimal tracking control Neural networks |
| 英文摘要 | This paper discusses a new policy iteration Q-learning algorithm to solve the infinite horizon optimal tracking problems for a class of discrete-time nonlinear systems. The idea is to use an iterative adaptive dynamic programming (ADP) technique to construct the iterative tracking control law which makes the system state track the desired state trajectory and simultaneously minimizes the iterative Q function. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem. The policy iteration Q-learning algorithm is then developed to obtain the optimal control law for the regulation system. Initialized by an arbitrary admissible control law, the convergence property is analyzed. It is shown that the iterative Q function is monotonically non-increasing and converges to the optimal Q function. It is proven that any of the iterative control laws can stabilize the transformed nonlinear system. Two neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of policy iteration Q-learning algorithm. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm. (C) 2015 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | DYNAMIC-PROGRAMMING ALGORITHM ; CONTROL SCHEME ; FEEDBACK-CONTROL ; TIME-SYSTEMS ; REINFORCEMENT ; APPROXIMATION ; GAMES ; DELAY |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S0925231215007730 |
| 语种 | 英语 |
| WOS记录号 | WOS:000359165000050 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8896]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 通讯作者 | Qinglai Wei |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 3.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Qinglai,Song, Ruizhuo,Sun, Qiuye,et al. Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm[J]. NEUROCOMPUTING,2015,168:520-528. |
| APA | Wei, Qinglai,Song, Ruizhuo,Sun, Qiuye,&Qinglai Wei.(2015).Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm.NEUROCOMPUTING,168,520-528. |
| MLA | Wei, Qinglai,et al."Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm".NEUROCOMPUTING 168(2015):520-528. |
入库方式： OAI收割
来源：自动化研究所
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