Estimation of nonparametric noise and FRF models for multivariable systems—Part I: Theory
Estimation of nonparametric noise and FRF models for multivariable systems—Part I: Theory
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DOI：
10.1016/j.ymssp.2009.08.009
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发表时间：
2010-04
影响因子：
8.4
通讯作者：
R. Pintelon;J. Schoukens;G. Vandersteen;K. Barbé
中科院分区：
文献类型：
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作者：
R. Pintelon;J. Schoukens;G. Vandersteen;K. Barbé
这一系列的两篇论文提出了一种估计由任意输入激励的多变量线性动态系统的非参数噪声和频率响应函数模型的方法。它扩展了 Schoukens 等人的结果。 (2006) [1] 以及 Schoukens 和 Pintelon (2009) [2] 从具有已知输入和噪声输出观测值的单输入、单输出系统（=输出误差问题），到输入和输出都受到噪声干扰的多输入、多输出系统（=变量误差问题）。在第一部分中，理论是针对线性动态多变量输出误差问题而发展的。结果得到了模拟的支持。与基于相关技术的经典谱分析的详细比较表明，所提出的程序更加稳健。在第二部分（Pintelon 等人，2009）[3] 中，该方法首先应用于非线性系统，以及广义输出误差框架内的参数识别。接下来，它被扩展到处理变量错误问题以及反馈中的识别。最后通过四个实际测量实例进行说明。
This series of two papers presents a method for estimating nonparametric noise and frequency response function models of multivariable linear dynamic systems excited by arbitrary inputs. It extends the results of Schoukens et al. (2006) [1] and Schoukens and Pintelon (2009) [2] from single input, single output systems with known input and noisy output observations (=outputerrorproblem), to multiple input, multiple output systems where both the input and output are disturbed by noise (=errors‐in‐variables problem). In Part I, the theory is developed for linear dynamic multivariable output error problems. The results are supported by simulations. A detailed comparison with the classical spectral analysis based on correlation techniques shows that the proposed procedures are more robust. In Part II (Pintelon et al., 2009) [3], the method first is applied to nonlinear systems, and parametric identification within a generalized output error framework. Next, it is extended to handle errors-in-variables problems, and identification in feedback. Finally, it is illustrated on four real measurement examples.