Title: Chaotic inertia weight and constriction factor-based PSO algorithm for BLDC motor drive control
Authors: Manoj Kumar Merugumalla; Prema Kumar Navuri
Addresses: Department of Electrical Engineering, Andhra University, Viskhapatnam, India ' Faculty of Electrical Engineering, Andhra University, Visakhapatnam, India
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.
Keywords: brushless direct current motor; particle swarm optimisation; PSO; PID controller; constriction factor; chaotic inertia weight.
DOI: 10.1504/IJPSE.2019.096673
International Journal of Process Systems Engineering, 2019 Vol.5 No.1, pp.30 - 52
Received: 02 Jan 2018
Accepted: 23 Mar 2018
Published online: 07 Dec 2018 *