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Novel iterative neural dynamic programming for data-based approximate optimal control design
Mu, Chaoxu1; Wang, Ding2; He, Haibo3
Source PublicationAUTOMATICA
2017-07-01
Volume81Pages:240-252
SubtypeArticle
AbstractAs 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.
KeywordIterative Neural Dynamic Programming (Indp) Data-based Control Approximate Optimal Control Heuristic Dynamic Programming (Hdp) Affine And non-Affine Nonlinear Systems
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.automatica.2017.03.022
WOS KeywordNONLINEAR-SYSTEMS ; REINFORCEMENT ; STABILIZATION ; CONVERGENCE ; EQUATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(51529701 ; US National Science Foundation(ECCS 1053717 ; Beijing Natural Science Foundation(4162065) ; 61520106009 ; CMMI 1526835) ; 61533008 ; U1501251 ; 61533017)
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000403513900028
Citation statistics
Cited Times:43[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15228
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.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
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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.
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