利用粒子群优化算法实现阻尼比和频率的精确识别

摘要：本文提出了一种利用粒子群优化算法辨识阻尼比和频率的方法。该方法将系统频率、阻尼比、幅值和相位的辨识问题转化为非线性优化问题，引入粒子群优化算法寻找全局最优解。基于粒子群优化的阻尼比和频率辨识方法不需要测量激励信号，原理简单，实现容易。仿真和实验结果表明：基于粒子群优化算法的阻尼比和频率辨识方法不受邻近模态耦合的影响。在无噪声条件下具有较高的辨识精度，随着信噪比的逐步降低，辨识精度开始逐步下降。用低通滤波器滤除高阶模态后，得到的脉冲响应信号对频率、阻尼比、幅值的辨识精度影响很小，对相位的辨识精度影响很大。

Exact identification of damping and frequency based on a particle swarm optimization algorithm

A novel approach for structure modal parameter identification was proposed here. The approach changed an identification problem to an optimal one. The global optimal solutions for the required parameters including frequency, damping ratio, amplitude and phase of a structure could be obtained by taking advantages of a particle swarm optimization (PSO). The results of numerical simulations showed that the accuracy of this method was comparatively higher, and the adjacent modal coupling had no effect on its accuracy. The FIR lowpass filter would influence the accuracy of phase' s i- dentification.