Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models
Meta-aircraft, in High-Altitude, Long-Endurance (HALE) unmanned aerial vehicle (UAV) applications, utilize a strategy of formation flying in the stratosphere and aerial docking in the troposphere to enhance flight range and gust resistance. This paper explores an aerial docking strategy for unmanned...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/1/7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588655565209600 |
|---|---|
| author | Kangwen Sun Yixiang Gao Zhiyao Wang Haoquan Liang Chenxuan Zhao Xinzhe Ji |
| author_facet | Kangwen Sun Yixiang Gao Zhiyao Wang Haoquan Liang Chenxuan Zhao Xinzhe Ji |
| author_sort | Kangwen Sun |
| collection | DOAJ |
| description | Meta-aircraft, in High-Altitude, Long-Endurance (HALE) unmanned aerial vehicle (UAV) applications, utilize a strategy of formation flying in the stratosphere and aerial docking in the troposphere to enhance flight range and gust resistance. This paper explores an aerial docking strategy for unmanned meta-aircraft using a surrogate model based on aerodynamic data. The study begins with an analysis of the aerodynamic characteristics and the establishment of a dynamic model, followed by the development of a surrogate model using the vortex lattice method and a BP neural network. This model accurately simulates aerodynamic changes near the wingtip. Optimization of the docking process, focusing on impulse and moment of impulse, is achieved using a greedy algorithm. The results show a reduction in drag impulse and rolling moment by 10.89% and 15.76%, respectively, thereby easing the burden on the control system of UAVs. |
| format | Article |
| id | doaj-art-d4b40e3521ce414ebab91a04775647d2 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-d4b40e3521ce414ebab91a04775647d22025-01-24T13:29:37ZengMDPI AGDrones2504-446X2024-12-0191710.3390/drones9010007Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate ModelsKangwen Sun0Yixiang Gao1Zhiyao Wang2Haoquan Liang3Chenxuan Zhao4Xinzhe Ji5School of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, ChinaMeta-aircraft, in High-Altitude, Long-Endurance (HALE) unmanned aerial vehicle (UAV) applications, utilize a strategy of formation flying in the stratosphere and aerial docking in the troposphere to enhance flight range and gust resistance. This paper explores an aerial docking strategy for unmanned meta-aircraft using a surrogate model based on aerodynamic data. The study begins with an analysis of the aerodynamic characteristics and the establishment of a dynamic model, followed by the development of a surrogate model using the vortex lattice method and a BP neural network. This model accurately simulates aerodynamic changes near the wingtip. Optimization of the docking process, focusing on impulse and moment of impulse, is achieved using a greedy algorithm. The results show a reduction in drag impulse and rolling moment by 10.89% and 15.76%, respectively, thereby easing the burden on the control system of UAVs.https://www.mdpi.com/2504-446X/9/1/7meta-aircraftunmanned aerial vehiclessurrogate modelneural networkaerial docking |
| spellingShingle | Kangwen Sun Yixiang Gao Zhiyao Wang Haoquan Liang Chenxuan Zhao Xinzhe Ji Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models Drones meta-aircraft unmanned aerial vehicles surrogate model neural network aerial docking |
| title | Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models |
| title_full | Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models |
| title_fullStr | Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models |
| title_full_unstemmed | Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models |
| title_short | Research on an Aerial Docking Strategy for Meta-UAVs Using Aerodynamic Data Surrogate Models |
| title_sort | research on an aerial docking strategy for meta uavs using aerodynamic data surrogate models |
| topic | meta-aircraft unmanned aerial vehicles surrogate model neural network aerial docking |
| url | https://www.mdpi.com/2504-446X/9/1/7 |
| work_keys_str_mv | AT kangwensun researchonanaerialdockingstrategyformetauavsusingaerodynamicdatasurrogatemodels AT yixianggao researchonanaerialdockingstrategyformetauavsusingaerodynamicdatasurrogatemodels AT zhiyaowang researchonanaerialdockingstrategyformetauavsusingaerodynamicdatasurrogatemodels AT haoquanliang researchonanaerialdockingstrategyformetauavsusingaerodynamicdatasurrogatemodels AT chenxuanzhao researchonanaerialdockingstrategyformetauavsusingaerodynamicdatasurrogatemodels AT xinzheji researchonanaerialdockingstrategyformetauavsusingaerodynamicdatasurrogatemodels |