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.

Applied Science Research Center Applied Science Private University Amman 11931 Jordan

College of Engineering University of Business and Technology 21448 Jeddah Saudi Arabia

Department of Computer Engineering Batman University Batman Turkey

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

Department of Electrical Engineering Graphic Era Dehradun 248002 India

ENET Centre CEET VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

Faculty of Electrical Engineering Sahand University of Technology Tabriz Iran

Graphic Era Hill University Dehradun 248002 India

Jadara University Research Center Jadara University Irbid Jordan

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