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arXiv · 1702.03539

Subspace Identification of Large-Scale 1D Homogeneous Networks

Chengpu Yu·Michel Verhaegen·Anders Hansson
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Abstract

This paper considers the identification of large-scale 1D networks consisting of identical LTI dynamical systems. A new subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The identification of the local system matrices (up to a similarity transformation) is done via a low dimensional subspace retrieval step that enables the estimation of the Markov parameters of a locally lifted system. Using the estimated Markov parameters, the state-space realization of a single subsystem in the network is determined. The low dimensional subspace retrieval step exploits various key structural properties that are present in the data equation such as a low rank property and a {\em two-layer} Toeplitz structure in the data matrices constructed from products of the system matrices. For the estimation of the system matrices of a single subsystem, it is formulated as a structured low-rank matrix factorization problem. The effectiveness of the proposed identification method is demonstrated by a simulation example.

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Chengpu Yu, Michel Verhaegen, Anders Hansson. 2017-02-12. Subspace Identification of Large-Scale 1D Homogeneous Networks. https://arxiv.org/abs/1702.03539

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