Intelligent Optimization Using Craziness Particle Swarm on Permanent Magnet Synchronous Motor

Abstract

A Proportional Integral Derivative (PID) controller in a synchronous motor is widely used because of its simple structure, robustness, strength and ease of use. The use of a PID controller requires proper parameter settings for optimal performance on the motor. The solution often used is the trial-error method to determine the correct parameters for the PID, but the results obtained do not make the PID controller optimal. Recently there have been many studies to optimize PID controllers wrong with intelligent methods. For this reason, this research will use the Craziness Particle Swarm Optimization (CRPSO) optimization method to optimize and determine the proper parameters of the PID. The CRPSO method is a method that provides an innovation to the velocity function of the particles distributed in the PSO method. From the simulation results, CRPSO performance is more optimal than PSO. From the correct PID parameter tuning results, a minimum overshoot response is obtained with several speed variations. In addition, an increase was also obtained in PMSM starting torque using CRPSO.