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An optimization method for dynamic identification based on short-duration maneuvering flight test data
Received date: 2016-09-26
Revised date: 2016-11-09
Online published: 2016-11-21
How to generate the accurate response at the frequency range of interest from the maneuvering is one of the significant problems for dynamic identification. Although short-duration doublet is commonly used in flight test activities and is easy to implement with a good consideration of both security and economy, it is difficult to get the accurate estimation due to its limited spectrum and lower signal to noise ratio. Thus, a method focusing on improving estimation accuracy based on the short-duration maneuvering flight test data is developed in this paper. The main factors that affect the accuracy of the non-parametric model identification in the classical Welch method of spectrum estimate are analyzed. A novel data pre-processing method which can taper the window function edge is presented, and the multi-window composite technology is integrated to improve the identification accuracy. Regarding the limited frequency spectrum, an adaptive low-order equivalent matching method is developed to select the frequency range and node based on a weighted function of coherence function and power spectral density. This method make the low-order equivalent matching highly correlated with the maneuvering input signal, and improve the accuracy, consistency and adaptability of the model parameter identification. The dynamic identification optimization technique is applied to a large amount of short-duration maneuvering and several sweeping flight test data for various types of aircrafts. The results meet the accuracy requirement for flight quality evaluation application, while the algorithm is stable and reliable.
WANG Baoyin , ZHANG Shuguang , JIA Xiaopeng . An optimization method for dynamic identification based on short-duration maneuvering flight test data[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017 , 38(6) : 120815 -120815 . DOI: 10.7527/S1000-6893.2016.0293
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