Data-driven thrust prediction for UAV powertrains using artificial neural networks

Goli S., KURTULUŞ D. F., Waqar M., Imran I. H., Kouser T., Memon A. M., ...Daha Fazla

Neural Computing and Applications, cilt.38, sa.8, 2026 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 8
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s00521-026-12003-4
  • Dergi Adı: Neural Computing and Applications
  • Derginin Tarandığı İndeksler: Scopus, Compendex, Index Islamicus, INSPEC, zbMATH
  • Anahtar Kelimeler: Artificial neural network, Data-driven modelling, Electric powertrain systems, Propeller configurations, Thrust prediction, UAV powertrain
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

This study develops an Artificial Neural Network (ANN)-based model to predict thrust in electric powertrain systems of Unmanned Aerial Vehicles (UAVs), aiming to reduce dependence on extensive experimental testing. Traditional thrust estimation approaches often require resource-intensive setups or simplified theoretical models with limited accuracy. To address this, an ANN model was trained on a comprehensive dataset covering 32 propeller configurations with varying diameters, pitches, and materials. The model, optimized using the Scaled Conjugate Gradient (SCG) algorithm, achieved high predictive accuracy with an R2 value of 0.998 and low errors across key metrics. Additionally, new propeller configurations were generated through data interpolation, enabling thrust prediction without additional physical tests. The results demonstrate that ANN-based modeling provides a reliable, cost-effective, and scalable alternative to conventional methods, supporting faster evaluation and design of UAV powertrain systems.