Population Algorithms for optimal control of BLDC motor drive

Abstract

This paper presents nature-inspired optimization algorithms such as particle swarm optimization (PSO) algorithm and bat algorithm (BA) for tuning PID controller parameters of BLDC motor drive. Both PSO algorithm BA are population based algorithms. Population based algorithms have number of advantages over classical methods for solving complex optimization problems. The position of BLDC rotor is determined by measuring the changes in the Back emf. Sensorless control method reduces the cost of motor as it does not need sensors for the detection of rotor position. The BLDC motor drive is modelled in Matlab/simullink. The simulation results reveals that proposed methods are effective in reducing the time domain parameters steady state error, rise time, settling time and peak overshoot.

Hybrid teaching-learning with comprehensive learning capability for electromagnetic device design problems

“…For example, Potnuru et al 19 presented a new developed nature-inspired flower pollination algorithm to design the speed control of BLDC motor with optimal PID tuning and successfully demonstrated its effectiveness by comparing with other methods. Merugumalla 20 optimized the PID controller parameters of the BLDC motor drive using PSO and BA. Sadrossadat and Rahmani 21 proposed an artificial neural network (ANN)-based L-P metric technique for parameter optimization of BLDC motors.…”