Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network
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 learni...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/12/6/540 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849472746346512384 |
|---|---|
| author | Xin Bao Yan Li Zhong Wang |
| author_facet | Xin Bao Yan Li Zhong Wang |
| author_sort | Xin Bao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ec0008d49b054b16bb0429e116286730 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-ec0008d49b054b16bb0429e1162867302025-08-20T03:24:26ZengMDPI AGAerospace2226-43102025-06-0112654010.3390/aerospace12060540Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural NetworkXin Bao0Yan Li1Zhong Wang2School of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaAutonomous 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.https://www.mdpi.com/2226-4310/12/6/540autonomous aerial refueling (AAR)drogue position predictionphysics-informed neural network (PINN)attention-augmented long short-term memory (AALSTM)drogue dynamic equations |
| spellingShingle | Xin Bao Yan Li Zhong Wang Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network Aerospace autonomous aerial refueling (AAR) drogue position prediction physics-informed neural network (PINN) attention-augmented long short-term memory (AALSTM) drogue dynamic equations |
| title | Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network |
| title_full | Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network |
| title_fullStr | Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network |
| title_full_unstemmed | Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network |
| title_short | Prediction of the Drogue Position in Autonomous Aerial Refueling Based on a Physics-Informed Neural Network |
| title_sort | prediction of the drogue position in autonomous aerial refueling based on a physics informed neural network |
| topic | autonomous aerial refueling (AAR) drogue position prediction physics-informed neural network (PINN) attention-augmented long short-term memory (AALSTM) drogue dynamic equations |
| url | https://www.mdpi.com/2226-4310/12/6/540 |
| work_keys_str_mv | AT xinbao predictionofthedroguepositioninautonomousaerialrefuelingbasedonaphysicsinformedneuralnetwork AT yanli predictionofthedroguepositioninautonomousaerialrefuelingbasedonaphysicsinformedneuralnetwork AT zhongwang predictionofthedroguepositioninautonomousaerialrefuelingbasedonaphysicsinformedneuralnetwork |