Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation
Joint Authors
Wong, Ka In
Wong, Pak-kin
Vong, Chi Man
Gao, Xiang Hui
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wong, Pak-kin& Vong, Chi Man& Gao, Xiang Hui& Wong, Ka In. 2014. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-457048
Modern Language Association (MLA)
Wong, Pak-kin…[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 No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-457048
American Medical Association (AMA)
Wong, Pak-kin& Vong, Chi Man& Gao, Xiang Hui& Wong, Ka In. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-457048
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457048