Novel iterative neural dynamic programming for data-based approximate optimal control design
文献类型：期刊论文
| 作者 | Mu, Chaoxu1; Wang, Ding2 ; He, Haibo3
|
| 刊名 | AUTOMATICA
 |
| 出版日期 | 2017-07-01 |
| 卷号 | 81页码:240-252 |
| 关键词 | Iterative Neural Dynamic Programming (Indp) Data-based Control Approximate Optimal Control Heuristic Dynamic Programming (Hdp) Affine And non-Affine Nonlinear Systems |
| DOI | 10.1016/j.automatica.2017.03.022 |
| 文献子类 | Article |
| 英文摘要 | As a powerful method of solving the nonlinear optimal control problem, the iterative adaptive dynamic programming (IADP) is usually established on the known controlled system model and is particular for affine nonlinear systems. Since most nonlinear systems are complicated to establish accurate mathematical models, this paper provides a novel data-based approximate optimal control algorithm, named iterative neural dynamic programming (INDP) for affine and non-affine nonlinear systems by using system data rather than accurate system models. The INDP strategy is built within the framework of IADP, where the convergence guarantee of the iteration is provided. The INDP algorithm is implemented based on the model-based heuristic dynamic programming (HDP) structure, where model, action and critic neural networks are employed to approximate the system dynamics, the control law and the iterative cost function, respectively. During the back-propagation of action and critic networks, the approach of directly minimizing the iterative cost function is developed to eliminate the requirement of establishing system models. The neural network implementation of the INDP algorithm is presented in detail and the associated stability is also analyzed. Simulation studies are conducted on affine and non-affine nonlinear systems, and further on the manipulator system, where all results have demonstrated the effectiveness of the proposed data-based approximate optimal control method. (C) 2017 Elsevier Ltd. All rights reserved. |
| WOS关键词 | NONLINEAR-SYSTEMS ; REINFORCEMENT ; STABILIZATION ; CONVERGENCE ; EQUATION |
| WOS研究方向 | Automation & Control Systems ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000403513900028 |
| 资助机构 | National Natural Science Foundation of China(51529701 ; US National Science Foundation(ECCS 1053717 ; Beijing Natural Science Foundation(4162065) ; 61520106009 ; CMMI 1526835) ; 61533008 ; U1501251 ; 61533017) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15228]  |
| 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 | 1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA |
| 推荐引用方式 GB/T 7714 | Mu, Chaoxu,Wang, Ding,He, Haibo. Novel iterative neural dynamic programming for data-based approximate optimal control design[J]. AUTOMATICA,2017,81:240-252. |
| APA | Mu, Chaoxu,Wang, Ding,&He, Haibo.(2017).Novel iterative neural dynamic programming for data-based approximate optimal control design.AUTOMATICA,81,240-252. |
| MLA | Mu, Chaoxu,et al."Novel iterative neural dynamic programming for data-based approximate optimal control design".AUTOMATICA 81(2017):240-252. |
入库方式： OAI收割
来源：自动化研究所
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