Subspace identification of local systems in 1D homogeneous networks

Abstract

This note considers the identification of large-scale 1D networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and a two-layer Toeplitz structural property in the data equation, while the state-space realization of a single subsystem is formulated as a structured low-rank matrix factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.