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Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine
Wong,Pak Kin1; Huang,Wei1; Vong,Chi Man2; Yang,Zhixin1
2020-09-01
Source PublicationNeural Computing and Applications
ISSN0941-0643
Volume32Issue:18Pages:14399-14409
AbstractThe automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load.
KeywordAdaptive neural control Engine idle speed regulation Extreme learning machine Uncertain nonlinearity
DOI10.1007/s00521-019-04482-5
URLView the original
Language英语
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorWong,Pak Kin
Affiliation1.Department of Electromechanical Engineering,University of Macau,Taipa,Macao
2.Department of Computer and Information Science,University of Macau,Taipa,Macao
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wong,Pak Kin,Huang,Wei,Vong,Chi Man,et al. Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine[J]. Neural Computing and Applications,2020,32(18):14399-14409.
APA Wong,Pak Kin,Huang,Wei,Vong,Chi Man,&Yang,Zhixin.(2020).Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine.Neural Computing and Applications,32(18),14399-14409.
MLA Wong,Pak Kin,et al."Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine".Neural Computing and Applications 32.18(2020):14399-14409.
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