Event-Driven Nonlinear Discounted Optimal Regulation Involving a Power System Application
Wang, Ding1,2,3; He, Haibo3; Zhong, Xiangnan3; Liu, Derong4

By employing neural network approximation architecture, the nonlinear discounted optimal regulation is handled under event-driven adaptive critic framework. The main idea lies in adopting an improved learning algorithm, so that the event-driven discounted optimal control law can be derived via training a neural network. The stability guarantee and simulation illustration are also included. It is highlighted that the initial stabilizing control policy is not required during the implementation process with the combined learning rule. Moreover, the closed-loop system is formulated as an impulsive model. Then, the related stability issue is addressed by using the Lyapunov approach. The simulation studies, including an application to a power system, are also conducted to verify the effectiveness of the present design method.

KeywordAdaptive/approximate Dynamic Programming Approximation Discount Factor Event-driven Control Neural Networks Optimal Regulation Power System
WOS HeadingsScience & Technology ; Technology
WOS KeywordTracking Control ; Time-systems
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(U1501251 ; Beijing Natural Science Foundation(4162065) ; U.S. National Science Foundation(ECCS 1053717) ; SKLMCCS ; 61533017 ; 61233001 ; 51529701 ; 61520106009)
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000410160200055
Citation statistics
Cited Times:48[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA
4.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Ding,He, Haibo,Zhong, Xiangnan,et al. Event-Driven Nonlinear Discounted Optimal Regulation Involving a Power System Application[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2017,64(10):8177-8186.
APA Wang, Ding,He, Haibo,Zhong, Xiangnan,&Liu, Derong.(2017).Event-Driven Nonlinear Discounted Optimal Regulation Involving a Power System Application.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,64(10),8177-8186.
MLA Wang, Ding,et al."Event-Driven Nonlinear Discounted Optimal Regulation Involving a Power System Application".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 64.10(2017):8177-8186.
Files in This Item: Download All
File Name/Size DocType Version Access License
[J-2017-TIE] Event-d(1126KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Ding]'s Articles
[He, Haibo]'s Articles
[Zhong, Xiangnan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Ding]'s Articles
[He, Haibo]'s Articles
[Zhong, Xiangnan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Ding]'s Articles
[He, Haibo]'s Articles
[Zhong, Xiangnan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: [J-2017-TIE] Event-driven nonlinear discounted optimal regulation involving a power system application.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.