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Keywords = drogue position prediction

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20 pages, 1803 KB  
Article
Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network
by Xin Bao, Yan Li and Zhong Wang
Aerospace 2025, 12(6), 540; https://doi.org/10.3390/aerospace12060540 - 14 Jun 2025
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Abstract
Autonomous aerial refueling (AAR) technology is of crucial importance in the aviation field. Accurately predicting the position of the refueling drogue is a core challenge in implementing this technology. An innovative method of a physics-informed neural network (PINN), a fusion of supervised learning [...] Read more.
Autonomous aerial refueling (AAR) technology is of crucial importance in the aviation field. Accurately predicting the position of the refueling drogue is a core challenge in implementing this technology. An innovative method of a physics-informed neural network (PINN), a fusion of supervised learning and unsupervised learning, integrating physical information with an attention-augmented long short-term memory (AALSTM) neural network is proposed. By constructing a physical model of the refueling drogue, accurate physical constraints are provided for the prediction model. Meanwhile, an AALSTM neural network architecture is designed to predict partial states of the refueling drogue and parameters of the dynamic model. An attention-augmented mechanism is introduced to enhance the ability to capture key information. Simulation experiments verify that introducing an attention-augmented mechanism based on the conventional LSTM can improve prediction accuracy. The PINN significantly outperforms the conventional LSTM method in prediction accuracy, providing strong support for the development of AAR technology. Full article
(This article belongs to the Special Issue New Sights of Intelligent Robust Control in Aerospace)
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