Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN
Fixed-wing vertical take-off and landing (VTOL) UAVs have received more and more attention in recent years, because they have the advantages of both fixed-wing UAVs and rotary-wing UAVs. To meet its large flight envelope, the VTOL UAV needs accurate measurement of airflow parameters, including angle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/1/417 |
| _version_ | 1797431136148783104 |
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| author | Xiaoda Li Yongliang Wu Xiaowen Shan Haofan Zhang Yang Chen |
| author_facet | Xiaoda Li Yongliang Wu Xiaowen Shan Haofan Zhang Yang Chen |
| author_sort | Xiaoda Li |
| collection | DOAJ |
| description | Fixed-wing vertical take-off and landing (VTOL) UAVs have received more and more attention in recent years, because they have the advantages of both fixed-wing UAVs and rotary-wing UAVs. To meet its large flight envelope, the VTOL UAV needs accurate measurement of airflow parameters, including angle of attack, sideslip angle and speed of incoming flow, in a larger range of angle of attack. However, the traditional devices for the measurement of airflow parameters are unsuitable for large-angle measurement. In addition, their performance is unsatisfactory when the UAV is at low speed. Therefore, for tail-sitter VTOL UAVs, we used a 5-hole pressure probe to measure the pressure of these holes and transformed the pressure data into the airflow parameters required in the flight process using an artificial neural network (ANN) method. Through a series of comparative experiments, we achieved a high-performance neural network. Through the processing and analysis of wind-tunnel-experiment data, we verified the feasibility of the method proposed in this paper, which can make more accurate estimates of airflow parameters within a certain range. |
| first_indexed | 2024-03-09T09:40:35Z |
| format | Article |
| id | doaj.art-3aac3271cee74778bc2ddbad9d4829f5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:40:35Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3aac3271cee74778bc2ddbad9d4829f52023-12-02T00:56:41ZengMDPI AGSensors1424-82202022-12-0123141710.3390/s23010417Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANNXiaoda Li0Yongliang Wu1Xiaowen Shan2Haofan Zhang3Yang Chen4Department of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of Aeronautics and Astronautics, Xihua University, Chengdu 610039, ChinaDepartment of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaCollege of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, ChinaSchool of Physics and Mechatronics Engineering, Longyan University, Longyan 364012, ChinaFixed-wing vertical take-off and landing (VTOL) UAVs have received more and more attention in recent years, because they have the advantages of both fixed-wing UAVs and rotary-wing UAVs. To meet its large flight envelope, the VTOL UAV needs accurate measurement of airflow parameters, including angle of attack, sideslip angle and speed of incoming flow, in a larger range of angle of attack. However, the traditional devices for the measurement of airflow parameters are unsuitable for large-angle measurement. In addition, their performance is unsatisfactory when the UAV is at low speed. Therefore, for tail-sitter VTOL UAVs, we used a 5-hole pressure probe to measure the pressure of these holes and transformed the pressure data into the airflow parameters required in the flight process using an artificial neural network (ANN) method. Through a series of comparative experiments, we achieved a high-performance neural network. Through the processing and analysis of wind-tunnel-experiment data, we verified the feasibility of the method proposed in this paper, which can make more accurate estimates of airflow parameters within a certain range.https://www.mdpi.com/1424-8220/23/1/417tail-sitter VTOLestimation of airflow parameterslarge angle of attack5-hole probeneural network |
| spellingShingle | Xiaoda Li Yongliang Wu Xiaowen Shan Haofan Zhang Yang Chen Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN Sensors tail-sitter VTOL estimation of airflow parameters large angle of attack 5-hole probe neural network |
| title | Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN |
| title_full | Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN |
| title_fullStr | Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN |
| title_full_unstemmed | Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN |
| title_short | Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN |
| title_sort | estimation of airflow parameters for tail sitter uav through a 5 hole probe based on an ann |
| topic | tail-sitter VTOL estimation of airflow parameters large angle of attack 5-hole probe neural network |
| url | https://www.mdpi.com/1424-8220/23/1/417 |
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