Speed control of Switched Reluctance Machine using fuzzy controller and neural network

SRMs (Switched Reluctance Motors) are becoming increasingly popular due to their numerous benefits. The SRM is made up of two parts: a stator with concentric windings and a rotor with no windings or permanent magnets. The SRM has a straightforward design that provides great reliability, fault tolerance and minimal production costs. However, the high torque ripples, running vibrations and acoustic noise are the major drawbacks of SRM. In this study, a PID, fuzzy controller and neural network have been proposed to control the speed of switched reluctance machine. The aim of this work is to overcome the above drawbacks. Then, it is shown that this solution presents the advantages of fast convergence time and minimal error between the desired speed and the true speed. Simulation in MATLAB-Simulink was used to demonstrate the effectiveness of the suggested controllers.