Torque Ripple Minimization in SRM Based on Advanced Torque Sharing Function Modified by Genetic Algorithm Combined with Fuzzy PSO

Abstract

This paper presents a new and improved Torque Sharing Function (TSF) to minimize torque ripple of Switched Reluctance Motor (SRM). This approach combined of three steps. At first step, Genetic Algorithm has been used to define the best Turn-on and Turn-off angel of phase current. At second step, a fuzzy logic controller system has been designed as a new TSF. Finally, at the last step, Particle Swarm Optimization (PSO) has been used to optimize Fuzzy membership function. The two main merits of this approach are that the proposed control algorithm can be used in wide speed ranges and also three-step-design and optimization makes this approach enable to perfectly results in smooth torque. The effectiveness of this approach has been verified through a simulation of four phase 8/6 SRM in Matlab/Simulink. Obtained result from simulation shown that the produced torque was high quality and its ripple was one-third of fuzzy TSF. This proposed method is very powerful to adapt itself for various kind of SRMs with different parameters.