Speed drives control using particle swarm optimization for PMSM drives

<p>The paper presents a contemporary method for controlling the speed of a permanent magnet synchronous machine (PMSM) by optimizing the parameters of a proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. This approach aims to enhance the robustness and dynamic performance of the drive system, resulting in improved accuracy and sensitivity to load changes and wide range of speed. The study evaluates two tuning techniques for the PI controller, which are the traditional trial-and-error method and the PSO optimization method. The performance of the PMSM is assessed based on speed response performance, including rise time, overshoot, and settling time. The PSOtuned controller significantly minimizes overshoot compared to the trialand-error method. And also achieves a shorter settling time, indicating a more stable response. However, the rise time is slightly longer with the PSO-tuned controller compared to the conventional tuning method just for the medium speed. For the rated speed, PSO still having shorter rise time compared to trial-and-error PI method. These findings imply that while the PSO method may result in a longer rise time, its overall advantages in reducing overshoot and settling time make it a more effective option for speed control in PMSMs. This is consistent with other research suggesting that PSO can outperform traditional methods in optimizing control parameters across various applications.</p>

Authors

Publication Details

Journal
Indonesian Journal of Electrical Engineering and Computer Science
Published
2025-05-07
DOI
https://doi.org/10.11591/ijeecs.v38.i3.pp1440-1449
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.00
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article

Speed drives control using particle swarm optimization for PMSM drives

Jurifa Mat Lazi, Mohd Ruzaini Hashim, Md Hairul Nizam Talib, Hyreil Anuar Kasdirin et al.
Indonesian Journal of Electrical Engineering and Computer Science
Sensorless Control of Electric Motors
article

Speed drives control using particle swarm optimization for PMSM drives

Jurifa Mat Lazi, Mohd Ruzaini Hashim, Md Hairul Nizam Talib, Hyreil Anuar Kasdirin, Azrita Alias
article en

Abstract

<p>The paper presents a contemporary method for controlling the speed of a permanent magnet synchronous machine (PMSM) by optimizing the parameters of a proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. This approach aims to enhance the robustness and dynamic performance of the drive system, resulting in improved accuracy and sensitivity to load changes and wide range of speed. The study evaluates two tuning techniques for the PI controller, which are the traditional trial-and-error method and the PSO optimization method. The performance of the PMSM is assessed based on speed response performance, including rise time, overshoot, and settling time. The PSOtuned controller significantly minimizes overshoot compared to the trialand-error method. And also achieves a shorter settling time, indicating a more stable response. However, the rise time is slightly longer with the PSO-tuned controller compared to the conventional tuning method just for the medium speed. For the rated speed, PSO still having shorter rise time compared to trial-and-error PI method. These findings imply that while the PSO method may result in a longer rise time, its overall advantages in reducing overshoot and settling time make it a more effective option for speed control in PMSMs. This is consistent with other research suggesting that PSO can outperform traditional methods in optimizing control parameters across various applications.</p>

Indonesian Journal of Electrical Engineering and Computer ScienceVol. 38(3)
Openalex Percentile: Top 92%
Sensorless Control of Electric Motors
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Speed drives control using particle swarm optimization for PMSM drives — Jurifa Mat Lazi, Mohd Ruzaini Hashim, et al. · Indonesian Journal of Electrical Engineering and Computer Science (2025) | TGRS Research Map | TGRS