Logo

Design optimization of a bldc motor by genetic algorithm and simulated annealing

K.S.R., Rao and A.H.B., Othman (2007) Design optimization of a bldc motor by genetic algorithm and simulated annealing. In: 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007, 25 November 2007 through 28 November 2007, Kuala Lumpur.

[img] PDF
Restricted to Registered users only

11Kb

Official URL: http://www.scopus.com/inward/record.url?eid=2-s2.0...

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. ©2007 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Annealing; Brushless DC motors; Combinatorial mathematics; Combinatorial optimization; Constrained optimization; Constraint theory; Control systems; DC generator motors; DC motors; Design; Diesel engines; Dynamic programming; Electric motors; Functions; Genetic algorithms; Genetic engineering; Linearization; Magnets; Mathematical models; Motors; Optimal systems; Optimization; Particle size analysis; Population statistics; BLDC motors; Cross overs; Design optimizations; Design procedures; Non-linear programming; Objective functions; Optimal design parameters; Optimal designs; Optimal parameters; Population sizes; Steady states; Stochastic combinatorial optimizations; Simulated annealing
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:455
Deposited By: Assoc Prof Dr K. S. Rama Rao
Deposited On:09 Mar 2010 02:00
Last Modified:19 Jan 2017 08:27

Repository Staff Only: item control page

Document Downloads

More statistics for this item...