Particle Swarm Optimization tuned BELBIC controller for 8/6 SRM operation

Abstract

SRM motors due to simple mechanical design have significant role in high speed operations and hence require faster control of rotor speed and minimized torque ripple. Proposed work uses Particle Swarm Optimization (PSO) for the tuning of the Emotional Learning controller (BELBIC). PSO is used for tuning the training coefficients of the BELBIC and maximizing the reward of the system to provide minimized speed settling time. The simulation is performed on an 8/6 SRM in MATLAB r2012a version. The operation of 8/6 SRM motor is compared by using a simple PID controller, a BELBIC Controller and a PSO tuned BELBIC controller. PSO tuned BELBIC controller shows higher operational efficiency compared to the other two methods.