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Longitudinal and lateral aerodynamic characterisation of reflex wing Unmanned Aerial Vehicle from flight tests using Maximum Likelihood, Least Square and Neural Gauss Newton methods

Published online by Cambridge University Press:  10 September 2019

S. Saderla Open the ORCID record for S. Saderla [Opens in a new window] ,
R. Dhayalan ,
K. Singh ,
N. Kumar  and
A. K. Ghosh
S. Saderla*
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
R. Dhayalan
Affiliation:
Department of Aerospace EngineeringIndian Institute of Space Science and TechnologyTrivandrumIndia
K. Singh
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
N. Kumar
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
A. K. Ghosh
Affiliation:
Department of Aerospace EngineeringIIT KanpurKanpurIndia
*
saderlas@iitk.ac.in
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Abstract

In this paper, longitudinal and lateral-directional aerodynamic characterisation of the Cropped Delta Reflex Wing (CDRW) configuration–based unmanned aerial vehicle is carried out by means of full-scale static wind-tunnel tests followed by full-scale flight testing. A predecided set of longitudinal and lateral/directional manoeuvres is performed to acquire the respective flight data, using a dedicated onboard flight data acquisition system. The compatibility of the acquired dynamics is quantified, in terms of scale factors and biases of the measured variables, using Kinematic consistency check. Maximum likelihood (ML), least squares and newly emerging neural Gauss–Newton (NGN) methods were implemented for a wing-alone delta configuration, mainly to capture the dynamic derivatives for both longitudinal and lateral directional cases. Estimated damping and weak dynamic derivatives, which are in general challenging to capture for a wing alone configuration, are consistent using ML and NGN methods. Validation of the estimated parameters with aerodynamic model is performed by proof-of-match exercise and are presented therein.

Keywords

Reflex wingDelta configuration UAVParameter estimationMLLSNGN methods
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1269 , November 2019 , pp. 1807 - 1839
Copyright
© Royal Aeronautical Society 2019 

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