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

中文翻译：

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通过 Eagle Strategy 和 Firefly 和 PSO 优化无刷电机性能

无刷电机有特殊的地方，尽管有不同的电机可供选择，因为它具有无需换向、降低噪音和更长寿命等特点，BLDC 电机的实验参数跟踪可以通过开发一个参考系统来实现，并通过采用来保证其稳定性李雅普诺夫稳定性定理。但只有在自适应系统与强大高效的优化技术相结合的情况下才能保证稳定性。在本文中，强大的鹰策略与粒子群优化和萤火虫算法被应用于评估无刷电机的性能。Eagle Strategy(ES)使用Levys walk分布函数执行多样化的全局搜索，粒子群优化(PSO)和萤火虫算法(FFA)执行高效的密集局部搜索。组合运算使整体优化技术更加方便 仿真结果使用MATLAB Simulink软件获得