Measurement, Modeling, and Optimization Speed Control of BLDC Motor Using Fuzzy-PSO Based Algorithm

Abstract

Measurement, modeling, and optimization are three important components that must be done to get a better system on the BLDC motor speed control system. The problem that arises in the BLDC motor speed control system is the instability indicated by a high overshoot value, a slow rise time value, and a high error steady-state. The purpose of this study is to increase the stability indicator by eliminating the high value of overshoot and error steady-state and increasing the value of the rise time. The method used in this research is to measure the input and output physical parameters, to model the BLDC motor plant mathematically and the last is to perform optimization using several control methods such as Proportional Integral Derivative (PID) control, fuzzy logic intelligent control, and Particle Swarm Optimization algorithm. (PSO). Experimental and simulation results show that the PSO algorithm has a better value in increasing stability indicators when compared to the other two control methods with a rise time of 0.00121 seconds, settling time of 0.00241 seconds, and overshoot of 0%.