Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

Yousri, Dalia; Eteiba, Magdy B.; Zobaa, Ahmed F.; Allam, Dalia

doi:10.3390/app11031325

Open AccessArticle

Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

¹

Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt

²

College of Engineering, Design & Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(3), 1325; https://doi.org/10.3390/app11031325

Submission received: 4 January 2021 / Revised: 24 January 2021 / Accepted: 28 January 2021 / Published: 2 February 2021

(This article belongs to the Special Issue Modeling, Design and Control of Electric Machines)

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Abstract

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.

Keywords: chaos maps; Ensemble Particle Swarm Optimizer; Permanent Magnet Synchronous Motor

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MDPI and ACS Style

Yousri, D.; Eteiba, M.B.; Zobaa, A.F.; Allam, D. Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer. Appl. Sci. 2021, 11, 1325. https://doi.org/10.3390/app11031325

AMA Style

Yousri D, Eteiba MB, Zobaa AF, Allam D. Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer. Applied Sciences. 2021; 11(3):1325. https://doi.org/10.3390/app11031325

Chicago/Turabian Style

Yousri, Dalia, Magdy B. Eteiba, Ahmed F. Zobaa, and Dalia Allam. 2021. "Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer" Applied Sciences 11, no. 3: 1325. https://doi.org/10.3390/app11031325

APA Style

Yousri, D., Eteiba, M. B., Zobaa, A. F., & Allam, D. (2021). Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer. Applied Sciences, 11(3), 1325. https://doi.org/10.3390/app11031325

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Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

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