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

Creative Commons License

Jabari M., Izci D., Ekinci S., Bajaj M., Blazek V., Prokop L.

SCIENTIFIC REPORTS, cilt.15, sa.1, ss.12898, 2025 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1038/s41598-025-97070-8
  • Dergi Adı: SCIENTIFIC REPORTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.12898
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

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.