Torque ripple reduction in switched reluctance motor drives using B-spline neural networks

Zhengyu Lin, Donald S. Reay, Barry W. Williams, Xiangning He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented in this paper. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weights updating algorithm of the BSNN, the appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not required high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme. © 2005 IEEE.

Original languageEnglish
Title of host publicationConference Record of the 2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting
Pages2726-2733
Number of pages8
Volume4
DOIs
Publication statusPublished - 2005
Event2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting - Kowloon, Hong Kong, China
Duration: 2 Oct 20056 Oct 2005

Conference

Conference2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting
CountryChina
CityKowloon, Hong Kong
Period2/10/056/10/05

Keywords

  • B-spline neural network
  • Switched reluctance motor
  • Torque ripple

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