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<p>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 R<sup>2</sup> 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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Srikanth Goli,
  • Dilek Funda Kurtuluş,
  • Muhammad Waqar,
  • Imil Hamda Imran,
  • Taiba Kouser,
  • Azhar M. Memon,
  • Luai M. Alhems

摘要

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.