基于连续小波变换的飞行器结构模态参数辨识

给出了一种基于连续小波变换的多输入多输出MIMO(Multiple-Input Multiple-Output)飞行器结构模态参数的辨识方法.对结构离散运动方程进行连续小波变换建立了小波域内的系统AR(Auto Regression)模型,AR模型的系数矩阵决定着系统的动力学特性,可以通过最小二乘法求得.模态参数可以通过求解由AR系数矩阵构成的特征矩阵的特征值来获得.与已有基于小波变换的模态参数辨识方法相比,该方法应用了连续小波的时移共变性和小波变换的滤波能力来确保辨识的效率.在辨识过程中,采用优化算法提高了辨识的精度和稳定性.算例仿真结果表明该方法具有较高的计算精度和稳定性,能用于飞行器结构模态参数的辨识.

Modal parameters identification for flight vehicle based on the continuous wavelet transforms

A multi-input multi-output(MIMO) method for identifying flight vehicle structure modal parameters was proposed based on the continuous wavelet transforms.The auto regression(AR)model in the wavelet domain was established by applying the continuous wavelet transform to discrete equations of motion.The coefficient matrices of the AR model,which determine the dynamic characteristics of the system,were calculated through the least squares approach.The modal parameters were then computed by solving eigenvalues of eigenmatrix constructed by these coefficient matrices.Optimization algorithm was used to improving accuracy and stability of proposed approach in process of identification.The advantages of the proposed approach over the exiting methods of applying the wavelet transform to modal parameters identification are in use of the time invariance property and filtering ability of the transform to enhance the efficiency of identification.Simulation result shows this method is accurate and stable and can be used to identify flight vehicle structure modal parameters.