基于状态空间和NR-LMS的结构参数辨识方法

提出一种基于系统状态空间模型和归一化鲁棒最小均方根（NR-LMS， Normalized Robust Least Mean Square）理论的动力学结构参数辨识方法.利用系统的输入-输出数据建立其Hankel-Toeplitz模型，利用NR-LMS算法得到该模型参数的估计并求得系统的Hankel矩阵，对Hankel矩阵进行奇异值分解即可确定系统的阶次，进而确定系统状态空间模型的参数.仿真研究和实验结果表明，此方法可以准确、快速地提取出结构的参数，且抗噪能力较强.

State space theory and NR-LMS algorithm based method for structural dynamics parameter identification

A parameter identification method for structural dynamics system based on state space (SS) theory and normalized robust least mean square (NR-LMS) algorithm was proposed. By using this method, the identified dynamic system's input and output data were used to build its Hankel-Toeplitz model based on the state space theory. Iterative NR-LMS algorithm was applied to achieve parameters' estimates and Hankel matrix for this model. Singular value decomposition (SVD) method to Hankel matrix was employed for quantifying the order of this dynamic system. Modal parameters and the state space model's parameters also could be achieved from the Hankel matrix by certain calculation. A simulation of 3-DOF(degree of freedom) spring-mass system was employed to validate this method and experiment of identifying cantilever's parameters was studied. The results demonstrate this method can identify structural parameters accurately and quickly.