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

المؤلفون المشاركون

Wong, Ka In
Wong, Pak-kin
Vong, Chi Man
Gao, Xiang Hui

المصدر

Mathematical Problems in Engineering

العدد

المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-11، 11ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2014-04-07

دولة النشر

مصر

عدد الصفحات

11

التخصصات الرئيسية

هندسة مدنية

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-457048