Adaptive Control using Fully Online Sequential Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

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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.; Vong, C. M.; Gao, X.H.; Wong, K.I.
	2014-04-01
Source Publication	Mathematical Problems in Engineering (SCI-E)
ISSN	1024-123X
Pages	1-11
Abstract	Most adaptive neural control schemes are based on stochastic gradient-descent back-propagation (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, 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 was 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
Keyword	adaptive control engine air-fuel ratio control OS-ELM ReOS-ELM FOS-ELM
Language	英語English
The Source to Article	PB_Publication
PUB ID	11578
Document Type	Journal article
Collection	Faculty of Science and Technology
Corresponding Author	Vong, C. M.
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 (SCI-E), 2014, 1-11.
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 (SCI-E), 1-11.
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 (SCI-E) (2014):1-11.

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