UM  > 科技學院  > 機電工程系
Initial-Training-Free Online Sequential Extreme Learning Machine Based Adaptive Engine Air-fuel Ratio Control
Pak Kin Wong1; Xiang Hui Gao2; Ka In Wong3; Chi Man Vong4; Zhi-Xin Yang1
2018
Source PublicationInternational Journal of Machine Learning and Cybernetics
ISSN1868-8071
Abstract

In modern automotive engines, air–fuel ratio (AFR) strongly affects exhaust emissions, power, and brake-specific consumption. AFR control is therefore essential to engine performance. Most existing engine built-in AFR controllers, however, are lacking adaptive capability and cannot guarantee long-term control performance. Other popular AFR control approaches, like adaptive PID control or sliding mode control, are sensitive to noise or needs prior expert knowledge (such as the engine model of AFR). To address these issues, an initial-training-free online sequential extreme learning machine (ITF-OSELM) is proposed for the design of AFR controller, and hence a new adaptive AFR controller is developed. The core idea is to use ITF-OSELM for identifying the AFR dynamics in an online sequential manner based on the real-time engine data, and then use the ITF-OSELM model to calculate the necessary control signal, so that the AFR can be regulated. The contribution of the proposed approach is the integration of the initial-training-free online system identification algorithm in the controller design. Moreover, to guarantee the stability of the closed-loop control system, a stability analysis is also conducted. To verify the feasibility and evaluate the performance of the proposed AFR control approach, simulations on virtual engine and experiments on real engine have been carried out. Both results show that the proposed approach is effective for AFR regulation.

KeywordAutomotive Engine Air–fuel Ratio Online Sequential Extreme Learning Machine Adaptive Control
DOIhttps://www.doi.org/10.1007/s13042-018-0863-0
Indexed By其他
Language英语
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPak Kin Wong
Affiliation1.Department of Electromechanical EngineeringUniversity of MacauMacauChina
2.Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information ScienceHebei UniversityBaodingChina
3.Institute for the Development and QualityMacauChina
4.Department of Computer and Information ScienceUniversity of MacauMacauChina
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Pak Kin Wong,Xiang Hui Gao,Ka In Wong,et al. Initial-Training-Free Online Sequential Extreme Learning Machine Based Adaptive Engine Air-fuel Ratio Control[J]. International Journal of Machine Learning and Cybernetics,2018.
APA Pak Kin Wong,Xiang Hui Gao,Ka In Wong,Chi Man Vong,&Zhi-Xin Yang.(2018).Initial-Training-Free Online Sequential Extreme Learning Machine Based Adaptive Engine Air-fuel Ratio Control.International Journal of Machine Learning and Cybernetics.
MLA Pak Kin Wong,et al."Initial-Training-Free Online Sequential Extreme Learning Machine Based Adaptive Engine Air-fuel Ratio Control".International Journal of Machine Learning and Cybernetics (2018).
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Pak Kin Wong]'s Articles
[Xiang Hui Gao]'s Articles
[Ka In Wong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Pak Kin Wong]'s Articles
[Xiang Hui Gao]'s Articles
[Ka In Wong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Pak Kin Wong]'s Articles
[Xiang Hui Gao]'s Articles
[Ka In Wong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

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