Design optimization of a BLDC motor by Genetic Algorithm and Simulated Annealing

Abstract

This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a Brushless DC Motor (BLDC) widely used in many industrial motion control apparatus and systems. The design procedure of permanent magnet electronically commutated BLDC motor is much different from that of traditional motors. Single and multi-objective functions of the motor are derived based on the steady state mathematical model. A constrained optimization on the objective function is performed using Genetic Algorithm (GA) and Simulated Annealing (SA), and optimal parameters are obtained. The resulting effects of varying GA parameters such as population size, number of generations, and probability of mutation and crossover, are also presented. The optimal design parameters of the motor derived by GA are compared with those obtained by SA, another stochastic combinatorial optimization technique.