Neural networks used for torque ripple minimization from 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 neural techniques to the problem of torque ripple minimization in a switched reluctance motor (SRM) is presented. More conventional techniques for torque linearization and decoupling are reviewed, after which the application of a neural network to the problem is described. Results obtained experimentally and by simulation of a 4 kW IGBT converter and 4-phase SRM are used to illustrate the approach. The networks used have been implemented using both digital signal processor (DSP) and field programmable gate array (FPGA) technologies.

Original languageEnglish
Title of host publicationDrives II
Pages1-6
Number of pages6
Volume6
Edition377
Publication statusPublished - 1993
EventProceedings of the 5th European Conference on Power Electronics and Applications - Brighton, Engl
Duration: 13 Sep 199316 Sep 1993

Conference

ConferenceProceedings of the 5th European Conference on Power Electronics and Applications
CityBrighton, Engl
Period13/09/9316/09/93

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