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Initial-training-free online sequential extreme learning machine based adaptive engine air–fuel ratio control
Wong,Pak Kin1; Gao,Xiang Hui2; Wong,Ka In3; Vong,Chi Man4; Yang,Zhi Xin1
2019-09-01
Source PublicationInternational Journal of Machine Learning and Cybernetics
ISSN1868-8071
Volume10Issue:9Pages:2245-2256
AbstractIn 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.
KeywordAdaptive control Air–fuel ratio Automotive engine Online sequential extreme learning machine
DOI10.1007/s13042-018-0863-0
URLView the original
Language英语
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorWong,Pak Kin
Affiliation1.Department of Electromechanical Engineering,University of Macau,Macao
2.Key Laboratory of Machine Learning and Computational Intelligence,College of Mathematics and Information Science,Hebei University,Baoding,China
3.Institute for the Development and Quality,Macao
4.Department of Computer and Information Science,University of Macau,Macao
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wong,Pak Kin,Gao,Xiang Hui,Wong,Ka In,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,2019,10(9):2245-2256.
APA Wong,Pak Kin,Gao,Xiang Hui,Wong,Ka In,Vong,Chi Man,&Yang,Zhi Xin.(2019).Initial-training-free online sequential extreme learning machine based adaptive engine air–fuel ratio control.International Journal of Machine Learning and Cybernetics,10(9),2245-2256.
MLA Wong,Pak Kin,et al."Initial-training-free online sequential extreme learning machine based adaptive engine air–fuel ratio control".International Journal of Machine Learning and Cybernetics 10.9(2019):2245-2256.
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