Developing an algorithm for aerodynamic coefficients identification accuracy increase based on harmonic input signals

Аuthors

Moung H. O.^1*, Kyaw Z. L.^1**, Prihod'ko S. J.²

1. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
2. National Helicopter Center Mil & Kamov, 26/1, Garshina str., Tomilino, Moscow region, 140070, Russia

*e-mail: mounghtangom50@gmail.com
**e-mail: pinkesive@gmail.com

Abstract

The presented article deals with the problem of algorithm for aircraft aerodynamic coefficients accuracy increase on flight experiments data. All the signals employed while coefficients identification contain measurement noises, approximated by normally distributed random values with zero average and specified dispersions. A method of harmonic signals decomposition was considered to improve the identification accuracy.

As a rule, the flight modes, when control signals are being set by the pilot, are employed for identification. It is evident, that the high-accuracy reproduction of the signal wwaveform is impossible with manual control. Thus, the precise a priori knowledge of a test signal in this case is impossible, which determines the strong dependence from measuring errors. Therefore, the authors suggest apply a hardware-generated test signal, such as combination of two sine waves with different frequencies. For linear object, such double-frequency signal creates responses at the same two frequencies. Since we know now the exact shapes of the input and output signals, we can represent them as a composition of the four linearly independent harmonic components. The multiple regression method should be applied to obtain the coefficients. In this case it is highly efficient since reference harmonics are precisely known. Thus,

Thus, the basic regression analysis requirements are fulfilled, ensuring unbiased estimates of coefficients in the presence of the object signals’ measurement noise. Further, employing the decomposition coefficients, we reconstruct the signals, which are now to a high degree free from noise.

The article presents the results, demonstrating the high accuracy of coefficients identification at high levels of measurement noise.

Keywords:

identification, input and output signals, measurement, aerodynamic parameters, decomposition, measurement noise, identification estimates accuracy

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