Application of Soft Computing Techniques for Control of Switched Reluctance Motor SRM

Abstract

This thesis mainly contributes in the area of control of speed and torque ripple minimization of Switched Reluctance Motor (SRM) by application of Artificial Intelligence (AI) techniques for stable operation of the SRM drives. In this respect speed control and torque ripple minimization of 3-Phase SRM drive and 4-Phase SRM drive has been done. The performance improvement of the drive accomplished with the application of various improved Artificial Intelligence (AI) techniques, hybridization of AI techniques along with the new proposed controller structures. Switched Reluctance Motor attracts attention of many researchers of the world because of its many attractive characteristics such as inherent simplicity in construction, high torque to mass ratio, low maintenance cost makes it a strong competitor to induction machine. However the non linearities associated with magnetic characteristics of SRM results in high torque ripple vibration and acoustic noise. There is always challenge for researchers to minimise this torque ripple. The utilization and application of Switched Reluctance Motor (SRM) is fully explored with the advent and advancement of power electronics converter. SRM are electrically commutated machine. newline