飞机颤振模态参数识别方法研究
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摘要
颤振模态参数识别是当前飞机颤振试飞试验的一个重要课题。传统模态参数识别方法受低信噪比，密集模态、非线性等因素的影响往往无法给出准确的识别结果。为此，本文以新近发展的信号处理和系统辨识理论为基础，结合我国新型飞机颤振试飞的实际需求，研究并提出了颤振试验模态参数识别的新方法。其中的部分研究成果已成功应用于某型飞机的颤振试飞试验数据分析，产生了显著的军事和经济效益。本文的主要研究内容如下：
1 为提高小火箭激励条件下试飞试验数据的信噪比，提出了一种用于颤振飞行试验数据处理的小波去噪方法。该方法引入梯度倒数加权滤波器对数据进行预处理，处理后的数据运用冗余小波进行小波分解，然后对输入信号在不同尺度下分别进行阈值降噪，对输出信号则采用了一种改进的小波空域相关滤波法去噪。结果表明算法能有效提高试飞数据的信噪比。
2 为进一步提高脉冲激励响应信号的去噪效果，又提出了一种新的基于支持向量机的试飞响应数据去噪方法，该方法利用了脉冲响应的起始段数据信噪比较高的特点，将去噪问题转化成一个时间序列的预测问题，同时考虑到颤振信号的非平稳性，弱非线性以及数据长度较短的特点，采用支持向量机构造预测模型，获得了理想的去噪效果。
3 针对电传飞机采用的舵面扫频激励，分别提出了小波时频域去噪算法和分数阶傅立叶域去噪算法。二者皆利用了扫频信号在时频域内的聚焦特性，采用遮隔操作达到信噪分离的目的。所不同的是前者采用Morlet小波对扫频激励及其响应进行时频分析，在时频域内实现信噪分离。后者则是采用分数阶傅立叶变换，在分数阶傅立叶域内实现信噪分离。相比较于前者，后者的去噪效果更好，计算量更小。
4 为弥补传统最小二乘频域拟合辨识算法的不足，在整体最小二乘算法的基础上，将广义整体最小二乘和加权迭代广义整体最小二乘引入到模态参数辨识问题中，形成了频域内的整体最小二乘类辨识算法。该类算法利用线性的整体最小二乘类算法估计模型系数，避免了复杂的非线性优化计算及对初始值的
Flutter modal parameters identification plays an important role in flight flutter testing field. The traditional modal identification methods suffering from the low SNR (signal to noise ratio), close mode and nonlinear structure can't provide an accurate estimation of modal parameter (especially the damping ratio). For this reason, some new methods based on advanced signal processing and system identification theory are presented to meet the demand of new aircraft flight testing. The research results are partly applied to new aircraft flutter testing, and achieve great military and economy benefit. The main results of this thesis are described as follows:
1. A new wavelet denoising method is introduced for flight flutter test excited by rocket impulse. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A "clean" input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data.
2. A novel denoising method based on least-square support vector machines (LS-SVM) is proposed for flutter test excited by rocket impulse. The denoising procedure is implemented by time series prediction using LS-SVM, which is applicable to nonstationary and nonlinear impulse response data with short duration. Since SNR of impulse response varies with amplitude for the decaying property, the beginning data points with high SNR is used for training and prediction of the subsequent data with low SNR. Finally, the simulations and experiment on real flight test data demonstrate effectiveness and efficiency of this approach.
3. The control surface oscillated by sweep command is a new form excitation in our flight test and two time-frequency filtering methods are developed for new excitation. The common of the two methods is that they all utilize the
1 为提高小火箭激励条件下试飞试验数据的信噪比，提出了一种用于颤振飞行试验数据处理的小波去噪方法。该方法引入梯度倒数加权滤波器对数据进行预处理，处理后的数据运用冗余小波进行小波分解，然后对输入信号在不同尺度下分别进行阈值降噪，对输出信号则采用了一种改进的小波空域相关滤波法去噪。结果表明算法能有效提高试飞数据的信噪比。
2 为进一步提高脉冲激励响应信号的去噪效果，又提出了一种新的基于支持向量机的试飞响应数据去噪方法，该方法利用了脉冲响应的起始段数据信噪比较高的特点，将去噪问题转化成一个时间序列的预测问题，同时考虑到颤振信号的非平稳性，弱非线性以及数据长度较短的特点，采用支持向量机构造预测模型，获得了理想的去噪效果。
3 针对电传飞机采用的舵面扫频激励，分别提出了小波时频域去噪算法和分数阶傅立叶域去噪算法。二者皆利用了扫频信号在时频域内的聚焦特性，采用遮隔操作达到信噪分离的目的。所不同的是前者采用Morlet小波对扫频激励及其响应进行时频分析，在时频域内实现信噪分离。后者则是采用分数阶傅立叶变换，在分数阶傅立叶域内实现信噪分离。相比较于前者，后者的去噪效果更好，计算量更小。
4 为弥补传统最小二乘频域拟合辨识算法的不足，在整体最小二乘算法的基础上，将广义整体最小二乘和加权迭代广义整体最小二乘引入到模态参数辨识问题中，形成了频域内的整体最小二乘类辨识算法。该类算法利用线性的整体最小二乘类算法估计模型系数，避免了复杂的非线性优化计算及对初始值的
Flutter modal parameters identification plays an important role in flight flutter testing field. The traditional modal identification methods suffering from the low SNR (signal to noise ratio), close mode and nonlinear structure can't provide an accurate estimation of modal parameter (especially the damping ratio). For this reason, some new methods based on advanced signal processing and system identification theory are presented to meet the demand of new aircraft flight testing. The research results are partly applied to new aircraft flutter testing, and achieve great military and economy benefit. The main results of this thesis are described as follows:
1. A new wavelet denoising method is introduced for flight flutter test excited by rocket impulse. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A "clean" input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data.
2. A novel denoising method based on least-square support vector machines (LS-SVM) is proposed for flutter test excited by rocket impulse. The denoising procedure is implemented by time series prediction using LS-SVM, which is applicable to nonstationary and nonlinear impulse response data with short duration. Since SNR of impulse response varies with amplitude for the decaying property, the beginning data points with high SNR is used for training and prediction of the subsequent data with low SNR. Finally, the simulations and experiment on real flight test data demonstrate effectiveness and efficiency of this approach.
3. The control surface oscillated by sweep command is a new form excitation in our flight test and two time-frequency filtering methods are developed for new excitation. The common of the two methods is that they all utilize the
引文
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