Nonlinear Aerodynamic Model Identification for a Small Unmanned Aircraft from Flight Data

2021 ◽  
Author(s):  
James L. Gresham ◽  
Benjamin M. Simmons ◽  
Craig A. Woolsey
Keyword(s):  
Model Identification ◽  
Unmanned Aircraft ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Nonlinear Aerodynamic

scholarly journals Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

2016 ◽  
Vol 53 (5) ◽  
pp. 1261-1297 ◽  
Author(s):  
Jay M. Brandon ◽  
Eugene A. Morelli
Keyword(s):  
Real Time ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Nonlinear Aerodynamic

scholarly journals CALCULATION OF THE CHARACTERISTICS OF A UAV LAUNCH FROM A RAMP

Aviation ◽  
2014 ◽  
Vol 18 (4) ◽  
pp. 178-184 ◽  
Author(s):  
Valeriy Silkov ◽  
Andrii Zirka
Keyword(s):  
Unmanned Aircraft ◽  
Geometrical Parameters ◽  
Kinematic Parameters ◽  
Flight Data ◽  
Lift Off

A launch from a ramp is applied in order to increase the effectiveness of unmanned aircraft. To attain this goal, the kinematic parameters of movement during a UAV launch from a ramp are analyzed, formulas to determine these parameters are formulated, and a method for calculating rational geometrical parameters of a ramp, which provides a safe lift-off of a UAV with the specified flight data, is used. This paper considers the advantages of a UAV launch from a ramp and the development of algorithms that help to calculate the characteristics of a UAV launch from a ramp.

Physical Essence of Stall Phenomena and its WNN-Based Aerodynamic Modeling from Flight Data

2014 ◽  
Vol 602-605 ◽  
pp. 3140-3143
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Flight Data ◽  
Numerical Examples ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Aerodynamic Method ◽  
Physical Essence ◽  
Set Up

To understand the characteristics of aircraft stall for better aerodynamic model, the physical essence of the stall phenomena of aircraft is first introduced, and then a Wavelet Neural Network (WNN) is proposed to set up the stall aerodynamic model. Numerical examples indicates that through the deep cognition of the stall phenomena of aircraft the proposed stall aerodynamic method has a better accuracy than the traditional neural network and is also effective and feasible.

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