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Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation
Mu, Chaoxu1,2,3; Sun, Changyin2; Wang, Ding3; Song, Aiguo4
Source PublicationNEUROCOMPUTING
2017-10-18
Volume260Pages:432-442
SubtypeArticle
AbstractIn this paper, an adaptive tracking control scheme is designed for a class of continuous-time uncertain nonlinear systems based on the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Considering matched uncertainties, the tracking control of the continuous-time uncertain nonlinear system can be transformed to the optimal tracking control of the associated nominal system. By building the nominal error system and modifying its cost function, the solution of the relevant FIJB equation can be contributed to the adaptive tracking control of the continuous-time uncertain nonlinear system. In view of the complexity on solving the HJB equation, its approximate solution is pursued by the policy iteration algorithm under the adaptive dynamic programming (ADP) framework, where a critic neural network is constructed to approximate the optimal cost function, and an action network is used to directly calculate the approximate optimal control law, which constitutes the tracking control law for the original uncertain system together with the steady control law. The weight convergence of the critic network and the stability of the closed-loop system are provided as the theoretical guarantee based on the Lyapunov theory. Two simulation examples are studied to verify the theoretical results and the effectiveness of the proposed tracking control scheme. (C) 2017 Elsevier B.V. All rights reserved.
KeywordAdaptive Tracking Control Hamilton-jacobi-bellman (Hjb) Equation Adaptive Dynamic Programming (Adp) Neural Networks Uncertainties
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2017.04.043
WOS KeywordFEEDBACK-CONTROL ; DEAD-ZONE ; REINFORCEMENT ; INPUT ; DESIGN
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(U1501251 ; China Postdoctoral Science Foundation(2014M561559) ; Tianjin Natural Science Foundation(14JCQNJC05400) ; Beijing Natural Science Foundation(4162065) ; 61533008 ; 61533017 ; 61520106009)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000405536900044
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15278
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.Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Mu, Chaoxu,Sun, Changyin,Wang, Ding,et al. Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation[J]. NEUROCOMPUTING,2017,260:432-442.
APA Mu, Chaoxu,Sun, Changyin,Wang, Ding,&Song, Aiguo.(2017).Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation.NEUROCOMPUTING,260,432-442.
MLA Mu, Chaoxu,et al."Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation".NEUROCOMPUTING 260(2017):432-442.
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