Application of associative memory neural networks to the control of a switched reluctance motor

D. S. Reay, T. C. Green, B. W. Williams

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

Abstract

The application of an associative memory neural network to the problem of torque ripple minimisation in a switched reluctance motor is presented. Conventional techniques for torque linearisation and decoupling are reviewed, after which the application of neural techniques to the problem is described. An instrumented test rig based around a 4 kW IGBT converter and a four phase switched reluctance motor has been constructed. Results obtained experimentally and by simulation demonstrate the effectiveness of the approach. The neural network has been implemented using both digital signal processor and field programmable gate array technologies.

Original languageEnglish
Title of host publicationPlenary Session, Emerging Technologies, and Factory Automation
Pages200-206
Number of pages7
Volume1
Publication statusPublished - 1993
EventProceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation - Maui, Hawaii, USA
Duration: 15 Nov 199318 Nov 1993

Conference

ConferenceProceedings of the 19th International Conference on Industrial Electronics, Control and Instrumentation
CityMaui, Hawaii, USA
Period15/11/9318/11/93

Fingerprint Dive into the research topics of 'Application of associative memory neural networks to the control of a switched reluctance motor'. Together they form a unique fingerprint.

  • Cite this

    Reay, D. S., Green, T. C., & Williams, B. W. (1993). Application of associative memory neural networks to the control of a switched reluctance motor. In Plenary Session, Emerging Technologies, and Factory Automation (Vol. 1, pp. 200-206)