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Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation
Wong P.K.1; Vong C.M.2; Gao X.H.1; Wong K.I.1
2014
Source PublicationMathematical Problems in Engineering
ISSN1024123X
Volume2014
Abstract

Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP), which suffers from local minima problem. Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM) can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning. The initial data is usually difficult to collect in adaptive control applications. Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM). While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications. To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model. Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance. A comparison among FOS-ELM and SGBP was also conducted. The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to "forget" what it has learnt. This implies that FOS-ELM is more preferable for adaptive control applications. © 2014 Pak Kin Wong et al.

DOI10.1155/2014/246964
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Mathematics
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications
WOS IDWOS:000334776700001
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorVong C.M.
Affiliation1.Univ Macau, Dept Electromech Engn, Taipa, Peoples R China
2.Univ Macau, Dept Comp & Informat Sci, Taipa, Peoples R China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wong P.K.,Vong C.M.,Gao X.H.,et al. Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation[J]. Mathematical Problems in Engineering,2014,2014.
APA Wong P.K.,Vong C.M.,Gao X.H.,&Wong K.I..(2014).Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation.Mathematical Problems in Engineering,2014.
MLA Wong P.K.,et al."Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation".Mathematical Problems in Engineering 2014(2014).
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