Switched reluctance motor control via fuzzy adaptive systems

Donald S. Reay, Mehran Mirkazemi-Moud, Tim C. Green, Barry W. Williams

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


This article presents the application of fuzzy adaptive systems to the problem of torque ripple reduction in a switched reluctance motor. Conventional methods for torque linearization and decoupling are reviewed briefly, as is the previous application, by the authors, of neural network based techniques. A solution based on the use of fuzzy adaptive systems is then described. Experimental measurements of the static torque production characteristics of a 4 kW, four-phase switched reluctance motor form the basis of simulation studies of this novel approach. The simulation results demonstrate the capability of fuzzy adaptive systems to learn non-linear current profiles that minimize torque ripple. The use of fuzzy systems in this application has potential advantages where the incorporation of a priori information, expressed linguistically, is concerned. Experimental results illustrate the effectiveness of the approach.

Original languageEnglish
Pages (from-to)8-15
Number of pages8
JournalIEEE Control Systems Magazine
Issue number3
Publication statusPublished - Jun 1995


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