A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints
Wang, Ding1; Liu, Derong1,2; Zhao, Dongbin1; Huang, Yuzhu1; Zhang, Dehua1
Source PublicationNEURAL COMPUTING & APPLICATIONS
2013-02-01
Volume22Issue:2Pages:219-227
SubtypeArticle
AbstractIn this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.
KeywordAdaptive Critic Designs Adaptive Dynamic Programming Approximate Dynamic Programming Neural Dynamic Programming Neural Networks Optimal Control Reinforcement Learning
WOS HeadingsScience & Technology ; Technology
WOS KeywordADAPTIVE CRITIC DESIGNS ; DISCRETE-TIME-SYSTEMS ; FEEDBACK-CONTROL ; REINFORCEMENT ; ADP
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000313657600004
Citation statistics
Cited Times:25[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3518
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China
2.Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Ding,Liu, Derong,Zhao, Dongbin,et al. A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints[J]. NEURAL COMPUTING & APPLICATIONS,2013,22(2):219-227.
APA Wang, Ding,Liu, Derong,Zhao, Dongbin,Huang, Yuzhu,&Zhang, Dehua.(2013).A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints.NEURAL COMPUTING & APPLICATIONS,22(2),219-227.
MLA Wang, Ding,et al."A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints".NEURAL COMPUTING & APPLICATIONS 22.2(2013):219-227.
Files in This Item: Download All
File Name/Size DocType Version Access License
[J-2013-NCA] A neura(507KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Ding]'s Articles
[Liu, Derong]'s Articles
[Zhao, Dongbin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Ding]'s Articles
[Liu, Derong]'s Articles
[Zhao, Dongbin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Ding]'s Articles
[Liu, Derong]'s Articles
[Zhao, Dongbin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: [J-2013-NCA] A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.