Switched reluctance motor control via fuzzy adaptive systems

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.