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Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems
文献类型：期刊论文
| 作者 | Liu, Derong; Wei, Qinglai
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2014-03-01 |
| 卷号 | 25期号:3页码:621-634 |
| 关键词 | Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming discrete-time policy iteration neural networks neurodynamic programming nonlinear systems optimal control reinforcement learning |
| 英文摘要 | This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | NETWORKED CONTROL-SYSTEM ; OPTIMAL TRACKING CONTROL ; ONLINE LEARNING CONTROL ; CONTROL SCHEME ; FEEDBACK-CONTROL ; CRITIC DESIGNS ; REINFORCEMENT ; APPROXIMATION ; ARCHITECTURE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000331985500014 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3840]  |
| 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 |
| 作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Derong,Wei, Qinglai. Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2014,25(3):621-634. |
| APA | Liu, Derong,&Wei, Qinglai.(2014).Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,25(3),621-634. |
| MLA | Liu, Derong,et al."Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 25.3(2014):621-634. |
入库方式： OAI收割
来源：自动化研究所
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