中文版 | English
Title

Large Angle of Attack Prediction for Tail-Sitter Using ANN-Based Flush Air Data Sensing

Author
Corresponding AuthorShan,Xiaowen
DOI
Publication Years
2023
ISSN
1876-1100
EISSN
1876-1119
Source Title
Volume
845 LNEE
Pages
6722-6731
Abstract
Flush air data sensing systems (FADS) have been widely applied on aerial vehicles to provide air data estimation. Air data such as angle of attack (AoA) and air speed can be estimated through resolving pressure measurements of the sensor matrix. These parameters can be utilized to improve the performance of flight control system and realize better flight performance. Existing FADS studies and applications can estimate AoA in the range typically below 55 . It is suitable for traditional fixed wing unmanned aerial vehicles (UAVs), but some fixed wing vertical take off and landing (VTOL) UAVs have requirements in measuring air data under larger AoA. In this work, a FADS based on artificial neural network has been applied on a tail-sitter to provided large AoA estimation in low Reynolds number. Computational fluid dynamic analysis has been carried out to evaluate the critical AoA where stall region affects the sensor matrix. Wind tunnel tests have been further carried to collect data for network training. The trained network can provide estimation of large AoA at the range of −80 to 80 with acceptable accuracy.
Keywords
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Scopus EID
2-s2.0-85151130293
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524283
DepartmentDepartment of Mechanics and Aerospace Engineering
前沿与交叉科学研究院
Affiliation
1.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Academy for Advanced Interdisciplinary Studies,Southern University of Science and Technology,Shenzhen,518055,China
First Author AffilicationDepartment of Mechanics and Aerospace Engineering
Corresponding Author AffilicationDepartment of Mechanics and Aerospace Engineering
First Author's First AffilicationDepartment of Mechanics and Aerospace Engineering
Recommended Citation
GB/T 7714
Tianchun,L. Y.,Li,Xiaoda,Wu,Yongliang,et al. Large Angle of Attack Prediction for Tail-Sitter Using ANN-Based Flush Air Data Sensing[C],2023:6722-6731.
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