Novel iterative neural dynamic programming for data-based approximate optimal control design

doi:10.1016/j.automatica.2017.03.022

CASIA OpenIR > 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

	Novel iterative neural dynamic programming for data-based approximate optimal control design
	Mu, Chaoxu 1; Wang, Ding2 ; He, Haibo 3
发表期刊	AUTOMATICA
	2017-07-01
卷号	81 页码:240-252
文章类型	Article
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.
关键词	Iterative Neural Dynamic Programming (Indp) Data-based Control Approximate Optimal Control Heuristic Dynamic Programming (Hdp) Affine And non-Affine Nonlinear Systems
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.automatica.2017.03.022
关键词[WOS]	NONLINEAR-SYSTEMS ; REINFORCEMENT ; STABILIZATION ; CONVERGENCE ; EQUATION
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(51529701 ; US National Science Foundation(ECCS 1053717 ; Beijing Natural Science Foundation(4162065) ; 61520106009 ; CMMI 1526835) ; 61533008 ; U1501251 ; 61533017)
WOS研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000403513900028
引用统计	被引频次：140[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15228
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
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.

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