Chaotic inertia weight and constriction factor-based PSO algorithm for BLDC motor drive control

Abstract

The population algorithms have a number of advantages over classical methods for solving complex optimisation problems such as tuning of controller parameters of motor drives These algorithms for solving various problems of global optimisation is often called as methods inspired by nature, methods in this class are based on the modelling of intelligent behaviour of organised members of the population. Particle swarm optimisation (PSO) algorithm is population-based algorithm which has ability to fine tune the controller parameters. In this paper, chaotic inertia weight and constriction factor-based PSO algorithms are proposed for tuning of proportional-integral-derivative (PID) controller parameters to control brushless direct current (BLDC) motor drive. The BLDC is modelled in MATLAB/Simulink and trapezoidal back emf waveforms are modelled as a function of rotor position using MATLAB code. The simulation results of PSO algorithms are compared and results shown the effectiveness of C-inertia weight and C-factor in tuning PID controller parameters.