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<p>In the domain of unmanned aerial vehicles (UAVs), evaluating electric propulsion systems is pivotal for enhancing performance and efficiency. This study employs a scaled conjugate gradient (SCG) algorithm to train an artificial neural network (ANN) for the propulsion system evaluation, offering a cutting-edge alternative to traditional experimental methods. The ANN architecture consists of an input layer, a single hidden layer, and an output layer. By varying the number of neurons in the hidden layer from 1 to 100, the optimal configuration with 2 neurons was identified, achieving high predictive accuracy. The model was trained using experimental datasets, predicting thrust force with an overall R<sup>2</sup> value exceeding 0.99 across training, validation, and testing phases, and a low overall prediction error of 1.27%. These results demonstrate the ANN’s capability to generalize from training data, making it a valuable tool for UAV designers. Integrating ANN-based evaluations accelerates decision-making processes and optimizes UAV performance, marking a significant advancement in UAV technology.</p>

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Artificial neural network for the evaluation of electric propulsion system in unmanned aerial vehicles

  • 期刊论文

  • 发表日期：

    2025年2月21日

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

摘要

In the domain of unmanned aerial vehicles (UAVs), evaluating electric propulsion systems is pivotal for enhancing performance and efficiency. This study employs a scaled conjugate gradient (SCG) algorithm to train an artificial neural network (ANN) for the propulsion system evaluation, offering a cutting-edge alternative to traditional experimental methods. The ANN architecture consists of an input layer, a single hidden layer, and an output layer. By varying the number of neurons in the hidden layer from 1 to 100, the optimal configuration with 2 neurons was identified, achieving high predictive accuracy. The model was trained using experimental datasets, predicting thrust force with an overall R2 value exceeding 0.99 across training, validation, and testing phases, and a low overall prediction error of 1.27%. These results demonstrate the ANN’s capability to generalize from training data, making it a valuable tool for UAV designers. Integrating ANN-based evaluations accelerates decision-making processes and optimizes UAV performance, marking a significant advancement in UAV technology.