PSO and firefly algorithms based control of BLDC motor drive

Merugumalla, Manoj Kumar; Navuri, Prema Kumar

doi:10.1109/icisc.2018.8398951

Cited by 7 publications

(8 citation statements)

References 4 publications

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“…Step 3: Calculating the pbest, which represents the value of the objective function of every one of the particles in the current iteration's population is compared to the preceding iteration and the particle position that has a lower objective function value as pbest for the Current iteration has been stated [15]:…”

Section: Pi Speed Controllersmentioning

confidence: 99%

“…Step 5: Velocity update, after calculating gbest and pbest, the particles' velocity for the following iteration must be updated with the use of equation [15]:…”

Section: Pi Speed Controllersmentioning

confidence: 99%

“…Omega wolves use alpha, beta, and delta sites to update their sites. The first ideal solution is alpha, while 2 nd and 3 rd ideal solutions are respectively (beta) and (delta) [7][8][9][10][11][12][13][14][15][16].…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

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Design a PI Controller Based on PSO and GWO for a Brushless DC Motor

Abedulabbas¹,

Yaseen²

2022

JESA

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Recently all the moving mechanical parts that are subjected to wear and cause errors in the future are replaced with the equivalent of electrical. A Brushless Direct Current (BLDC) motor is preferable compared to a brushed DC motor because it substitutes the unit of mechanical commutations with an electronic unit, enhancing dynamic properties, noise level, and efficiency. Since it is fairly inexpensive, simple in structure, and performs well, maximum BLDC motor drives use a Proportional-Integral PI controller for controlling the machine's speed. The major issue with the PI controller, on the other hand, is altering its parameters throughout the deployment. As a result, this work shows how to tune the PI controller settings of a BLDC motor drive using Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). The results of a comparison of PSO and GWO for BLDC motors were obtained. Simulation tests for the BLDC engine in MATLAB/Simulink environment show that both PSO and GWO of BLDC motor give good results, but the best is GWO in tested in terms of transient response under different mechanical loads and speeds.

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Section: Pi Speed Controllersmentioning

confidence: 99%

“…Step 5: Velocity update, after calculating gbest and pbest, the particles' velocity for the following iteration must be updated with the use of equation [15]:…”

Section: Pi Speed Controllersmentioning

confidence: 99%

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Design a PI Controller Based on PSO and GWO for a Brushless DC Motor

Abedulabbas¹,

Yaseen²

2022

JESA

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“…Conventional control methods cannot resist these alterations and lose their precision. Thus, it was necessary to implement advanced control techniques to solve this problem, especially those based on the artificial intelligence, such as: fuzzy control [32,33], neural control [34,35], Genetic Algorithm (GA) control [36,37], PSO control [38], BAT control [31] and recently, FA control and Improved Firefly Algorithm (IFA) or Modified Firefly Algorithm (MFA) [24][25][26][27][28]. These methods are based essentially on the optimization of the PID corrector parameters and its derivatives to obtain optimal performance.…”

Section: Introductionmentioning

confidence: 99%

A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor

Bazi

Benzid

Bazi

et al. 2021

Sensors

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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.

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“…Merugumalla ve Navuri[30], yaptıkları çalışmada fırçasız doğru akım motor sürücüsünün denetimi için ateşböceği algoritması tabanlı PID denetleyici tasarlamış ve benzetimini gerçekleştirmişlerdir.Yazarlar benzetim sonuçlarını kararlı durum hatası, yükselme süresi, oturma süresi ve aşım gibi zaman domeni performans kriterlerini kullanarak analiz etmiştir.…”

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Ateş Böceği Algoritması Tabanlı PI Denetleyici Kullanarak Üç Fazlı Vienna Doğrultucunun Dinamik Performansının İncelenmesi

Keçecioğlu

Açıkgöz²

2020

DÜMF Mühendislik Dergisi

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Bu çalışmada, üç fazlı Vienna doğrultucunun DA-Bara geriliminin denetimi için metasezgisel algoritmalardan biri olan Ateş Böceği Algoritması (ABA) tabanlı bir PI denetleyici yapısı önerilmiştir. ABA-PI adı verilen önerilen denetleyici yapısı içerisindeki PI denetleyicinin kazanç katsayıları ABA ile belirlenmiştir. ABA-PI denetleyicinin giriş referansı, giriş gerilimi ve yük değişimlerine karşı dinamik performansını incelemek için Matlab/Simulink ortamında kullanılarak benzetim çalışmaları yapılmıştır. Yapılan benzetim çalışmaları sonucunda önerilen ABA-PI denetleyicinin tüm benzetim koşulları altında klasik PI denetleyiciye göre daha başarılı olduğu doğrulanmıştır.

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PSO and firefly algorithms based control of BLDC motor drive

Cited by 7 publications

References 4 publications

Design a PI Controller Based on PSO and GWO for a Brushless DC Motor

Design a PI Controller Based on PSO and GWO for a Brushless DC Motor

A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor

Ateş Böceği Algoritması Tabanlı PI Denetleyici Kullanarak Üç Fazlı Vienna Doğrultucunun Dinamik Performansının İncelenmesi

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