Brushless Motor Performance Optimization by Eagle Strategy with Firefly and PSO

TL;DR

This paper enhances brushless motor performance by integrating Eagle Strategy with PSO and Firefly algorithms for global and local optimization, verified through MATLAB simulations.

Contribution

It introduces a novel combination of Eagle Strategy with PSO and Firefly algorithms for optimized brushless motor performance.

Findings

Improved motor efficiency demonstrated in simulations

Enhanced global and local search capabilities achieved

Stable performance verified with Lyapunov stability criteria

Abstract

Brushless motors has special place though different motors are available because of its special features like absence in commutation, reduced noise and longer lifetime etc., The experimental parameter tracking of BLDC Motor can be achieved by developing a Reference system and their stability is guaranteed by adopting Lyapunov Stability theorems. But the stability is guaranteed only if the adaptive system is incorporated with the powerful and efficient optimization techniques. In this paper the powerful eagle strategy with Particle Swarm optimization and Firefly algorithms are applied to evaluate the performance of brushless motor Where, Eagle Strategy(ES) with the use of Levys walk distribution function performs diversified global search and the Particle Swarm Optimization (PSO) and Firefly Algorithm(FFA) performs the efficient intensive local search. The combined operation makes the overall optimization technique as much convenient The simulation results are obtained by using MATLAB Simulink software