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Application to the Control of BLDC Motor
Sensors ( IF 3.4 ) Pub Date : 2021-08-04 , DOI: 10.3390/s21165267
Smail Bazi 1, 2 , Redha Benzid 3 , Yakoub Bazi 4 , Mohamd Mahmoud Al Rahhal 5
Affiliation  

Firefly Algorithm (FA) is a recent swarm intelligence first introduced by X.S. Yang in 2008. It has been widely used to solve several optimization problems. Since then, many research works were elaborated presenting modified versions intending to improve performances of the standard one. Consequently, this article aims to present an accelerated variant compared to the original Algorithm. Through the resolving of some benchmark functions to reach optimal solution, obtained results demonstrate the superiority of the suggested alternative, so-called Fast Firefly Algorithm (FFA), when faced with those of the standard FA in term of convergence fastness to the global solution according to an almost similar precision. Additionally, a successful application for the control of a brushless direct current electric motor (BLDC) motor by optimization of the Proportional Integral (PI) regulator parameters is given. These parameters are optimized by the FFA, FA, GA, PSO and ABC algorithms using the IAE, ISE, ITAE and ISTE performance criteria.

中文翻译:

在BLDC电机控制中的应用

Firefly Algorithm (FA) 是最近由 XS Yang 于 2008 年首次引入的群体智能。它已被广泛用于解决多个优化问题。从那以后,许多研究工作被详细阐述,提出了旨在提高标准版本性能的修改版本。因此,本文旨在展示与原始算法相比的加速变体。通过解决一些基准函数以达到最优解,获得的结果证明了所建议的替代方案,即所谓的快速萤火虫算法(FFA)的优越性,当面对标准 FA 在对全局解的收敛速度方面,根据到几乎相似的精度。此外,给出了通过优化比例积分 (PI) 调节器参数来控制无刷直流电机 (BLDC) 电机的成功应用。这些参数由 FFA、FA、GA、PSO 和 ABC 算法使用 IAE、ISE、ITAE 和 ISTE 性能标准进行优化。
更新日期:2021-08-04
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