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结合传统控制器的混合自适应蚁狮优化算法用于驱动和控制开关磁阻电机以提高性能。

Hybrid adaptive ant lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance.

作者信息

Jabari Mostafa, Izci Davut, Ekinci Serdar, Bajaj Mohit, Blazek Vojtech, Prokop Lukas

机构信息

Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Department of Electrical and Electronics Engineering, Bursa Uludag University, 16059, Bursa, Turkey.

出版信息

Sci Rep. 2025 Apr 15;15(1):12898. doi: 10.1038/s41598-025-97070-8.

DOI:10.1038/s41598-025-97070-8
PMID:40234547
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12000315/
Abstract

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.

摘要

开关磁阻电机（SRM）因其耐用性、效率和成本效益而在工业应用中受到青睐，但面临诸如转矩脉动和非线性磁行为等挑战，这些挑战限制了它们在控制任务中的精度。为了解决这些问题，这项工作引入了一种新颖的混合自适应蚁狮优化（HAALO）算法，并结合PI和FOPID控制器，以提高开关磁阻电机的性能。HAALO算法通过集成自适应变异和精英保留技术进行动态实时控制，增强了传统蚁狮优化算法，优化了转矩脉动和速度调节。仿真结果证明了HAALO优化控制器相对于传统方法的优越性，显示出更快的收敛速度和更高的控制精度。本研究提供了一种新的混合优化方法，显著推进了开关磁阻电机控制，为高性能应用提供了有效的解决方案。

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