Hybrid adaptive ant lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257699" target="_blank" >RIV/61989100:27240/25:10257699 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27730/25:10257699
Výsledek na webu
<a href="https://www.nature.com/articles/s41598-025-96621-3" target="_blank" >https://www.nature.com/articles/s41598-025-96621-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-97070-8" target="_blank" >10.1038/s41598-025-97070-8</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Hybrid adaptive ant lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance
Popis výsledku v původním jazyce
Switched reluctance motors (SRMs) are favored in industrial applications for their durability, efficiency, and cost-effectiveness, yet face challenges such as torque ripple and nonlinear magnetic behavior that limit their precision in control tasks. To address these issues, this work introduces a novel hybrid adaptive ant lion optimization (HAALO) algorithm, combined with PI and FOPID controllers, to improve SRM performance. The HAALO algorithm enhances traditional ant lion optimization by integrating adaptive mutation and elite preservation techniques for dynamic real-time control, optimizing both torque ripple and speed regulation. Simulation results demonstrate the superiority of the HAALO-optimized controllers over conventional methods, showing faster convergence and enhanced control accuracy. This study provides a new hybrid optimization method that significantly advances SRM control, offering efficient solutions for high-performance applications.
Název v anglickém jazyce
Hybrid adaptive ant lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance
Popis výsledku anglicky
Switched reluctance motors (SRMs) are favored in industrial applications for their durability, efficiency, and cost-effectiveness, yet face challenges such as torque ripple and nonlinear magnetic behavior that limit their precision in control tasks. To address these issues, this work introduces a novel hybrid adaptive ant lion optimization (HAALO) algorithm, combined with PI and FOPID controllers, to improve SRM performance. The HAALO algorithm enhances traditional ant lion optimization by integrating adaptive mutation and elite preservation techniques for dynamic real-time control, optimizing both torque ripple and speed regulation. Simulation results demonstrate the superiority of the HAALO-optimized controllers over conventional methods, showing faster convergence and enhanced control accuracy. This study provides a new hybrid optimization method that significantly advances SRM control, offering efficient solutions for high-performance applications.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20200 - Electrical engineering, Electronic engineering, Information engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Scientific Reports
ISSN
2045-2322
e-ISSN
2045-2322
Svazek periodika
15
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
37
Strana od-do
1-37
Kód UT WoS článku
001468488100015
EID výsledku v databázi Scopus
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