Neural model of UAV unsteady aerodynamic characteristics from water tunnel tests data
Dariusz Rykaczewski , Mirosław Nowakowski , Andrzej Żyluk , Krzysztof Sibilski , Michal Garbowski , Wiesław Wróblewski
The paper presents an artificial neural network that represents coefficients of aerodynamic forces and moments of UAV model. Measurements was taken in the water tunnel during dynamic tests of short periodic oscillatory motion. Those measurements have been used to find the neural network modeling unsteady list and pitchng moment coefficient. The tunnel test program range were set up to high AOA and wide range of reduced frequencies of angular movements. Due to propeller built-in the model's structure test program were repeated for propelled drive system turned on. The identification method are used to generate model of flight dynamics of real UAV. Comparison between the experimentally obtained data and those from neural network is also given.
|Book||AIAA Atmospheric Flight Mechanics (AFM) Conference 2013, 2013|
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