Learning rate functions in CMAC neural network based control for torque ripple reduction of switched reluctance motors

Changjing Shang, Donald Reay, Barry Williams

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

5 Citations (Scopus)

Abstract

This paper presents a novel approach to adapting the weights of a CMAC neural network-based controllers for torque ripple reduction in switched reluctance motors. The proposed method modifies the conventional LMS algorithm using a varying learning rate which, for the present application, is defined as a function of the rotor angle of the motor under control. Simulation results demonstrate that developing CMAC network based adaptive controllers following this approach affords lower torque ripple with high power efficiency, whilst offering rapid learning convergence in system adaptation.

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Subtitle of host publicationProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4); Washington, DC, USA; ; 3 June 1996 through 6 June 1996
Pages2078-2083
Number of pages6
Volume4
Publication statusPublished - 1996
Event1996 IEEE International Conference on Neural Networks - Washington, DC, USA
Duration: 3 Jun 19966 Jun 1996

Conference

Conference1996 IEEE International Conference on Neural Networks
CityWashington, DC, USA
Period3/06/966/06/96

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