System identification (SI) techniques can be used to identify the dynamic parameters of mechanical systems and civil infrastructures. The aim is to rapidly and consistently model the object of interest, in a quantitative and principled manner. This is also useful in establishing the capacity of a structure to serve its purpose, thus as a tool for structural health monitoring (SHM). In this context, input–output SI techniques allow precise and robust identification regardless of the actual input. However, one of the most popular and widely used approaches, the Rational Fraction Polynomial (RFP) method, has several drawbacks. The fitting problem is nonlinear and generally non‐convex, with many local minima; even if linearised via weighting, it can become severely ill‐conditioned. Here, a novel proposal for the broadband macro‐modelling of structures in the frequency domain with several output and/or input channels is presented. A variant of the vector fitting approach, the Fast Relaxed Vector Fitting (FRVF), applied so far in the literature only for the identification of electrical circuits, is translated and adapted to serve as a technique for structural SI and compared with other traditional techniques. A study about the robustness of the FRVF method with respect to noise is carried out on a numerical system. Finally, the method is applied to two experimental case studies: a scaled model of a high‐aspect‐ratio (HAR) wing and the well known benchmark problem of the three‐storey frame of Los Alamos laboratories. Promising results were achieved in terms of accuracy and computational performance.

中文翻译：

---

快速松弛矢量拟合法对结构系统的实验模态分析

系统识别（SI）技术可用于识别机械系统和民用基础设施的动态参数。目的是以定量和有原则的方式快速一致地对感兴趣的对象进行建模。这对于建立结构实现其功能的能力也很有用，因此可以用作结构健康状况监视（SHM）的工具。在这种情况下，无论实际输入如何，输入输出SI技术都可以进行精确而可靠的标识。但是，最流行和使用最广泛的方法之一是有理分数多项式（RFP）方法，但有几个缺点。拟合问题是非线性的，通常是非凸的，具有许多局部最小值。即使通过加权线性化，也会变得病情严重。这里，提出了一种在频域中对具有多个输出和/或输入通道的结构进行宽带宏建模的新建议。到目前为止，仅在文献中仅用于识别电路的矢量拟合方法的一种变体，即快速松弛矢量拟合（FRVF），已被翻译并改编为用于结构SI的技术，并与其他传统技术进行了比较。在数值系统上研究了FRVF方法相对于噪声的鲁棒性。最后，该方法应用于两个实验案例研究：高纵横比（HAR）机翼的缩放模型和Los Alamos实验室三层框架的众所周知的基准问题。在准确性和计算性能方面取得了可喜的结果。