Hybrid Fuzzy–Bee expert system for energy-aware adaptive flight control of long-endurance unmanned aerial vehicles

CAN E.

Applied Soft Computing, cilt.204, 2027 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 204
  • Basım Tarihi: 2027
  • Doi Numarası: 10.1016/j.asoc.2026.116706
  • Dergi Adı: Applied Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC
  • Anahtar Kelimeler: Adaptive flight control, Bio-inspired algorithms, Energy harvesting, Fuzzy logic optimization, Piezo-thermo sensing
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

Unmanned Aerial Vehicles (UAVs) are often constrained by limited battery life, which restricts their ability to perform long-duration missions autonomously. This study introduces a hybrid approach that combines piezoelectric and thermoelectric energy-aware sensing with an intelligent Fuzzy Bee Hybrid Algorithm. The proposed framework simultaneously addresses flight control adaptation and energy management, aiming to improve UAV operational efficiency without replacing the main onboard power source. A dual-source energy module harnesses mechanical vibrations and temperature differences encountered during flight to generate auxiliary electrical signals, providing real-time information about environmental and structural conditions. These signals are integrated into the control system to enable energy-aware decision-making. The artificial intelligence contribution of this work lies in combining a fuzzy logic controller with the Bee Foraging Algorithm, allowing UAVs to adaptively adjust flight paths in response to dynamic conditions while maintaining stability. From an engineering perspective, the hybrid sensing and supplementary energy module enhances mission endurance by improving awareness of energy utilization. Overall, the proposed framework empowers UAVs to autonomously respond to changing environments, supporting the development of next-generation long-endurance UAV platforms.