The new Subspace-based poly-reference Complex Frequency (S-pCF) for robust frequency-domain modal parameter estimation

Abstract

Subspace identification has been widely used in modal parameter estimation. The formulation of a parametric subspace-driven modal identification technique is achieved by using the Singular Value Decomposition (SVD) to remove most the uncertainties present in the null space of the system matrices used in the identification process, which leads to a more robust estimation of the modal properties. This idea has been used in the formulation of time domain modal identification techniques like the (time-domain) Eigensystem Realization Algorithm and the Stochastic Subspace identification techniques. In this paper, a subspace implementation of the new poly-reference Complex Frequency (pCF) formulated in Modal Model is proposed. The idea is to combine SVD with the new pCF to derive a new system identification technique that operates in the frequency domain by using either the Frequency Response Function or the Half Spectrum as primary data. Similarly to the ERA, the idea behind the subspace implementation of the pCF is to use different subspaces of the observability and frequency-domain controllability matrices, and carry out eigen-system realizations to estimate the modal properties corresponding to each chosen subspace. In order to illustrate its robustness, the subspace-driven pCF is applied to a simulated and two practical application examples.