ON DESIGN OF AN ADAPTIVE PSO-FUZZY LOGIC-BASED SPEED CONTROL SCHEME FOR SWITCHED RELUCTANCE MOTOR DRIVES

Abstract

In this paper, Particle Swarm Optimization (PSO) method, which is one of the most effective biological-inspired optimization algorithms, will be applied to design an adaptive Fuzzy Logic (FL)-based speed control strategy for Switched Reluctance Motor (SRM) drives. The PSO mechanism is used to not only optimize three scaling factors of a PI-type FL speed controller but also determine efficiently two switching angles of an asymmetrical DC-DC voltage converter which would be highly consistent with the SRM feeding. The control methodology applying each of the five-optimal-parameter groups obtained is capable of achieving effective control performances for a particular SRM drive system, such as short rise time, non-overshoot and highly small reduction of the speed despite the occurrence of load torques. In addition, the starting and running torques are of high values to be suitable for designing an effective traction control system. It is found that these promising features are much better than those of the conventional PI-based scheme applied to the SRM drive system. To achieve the obvious validation with respect to the efficiency, feasibility and superiority of the proposed control strategy, numerical simulations for a typical three phase SRM drive with various cases of the load torques will also be performed using MATLAB/Simulink package.