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.
Original language | English |
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Pages (from-to) | 1126-1131 |
Journal | IEEE Transactions on Automatic Control |
Volume | 63 (2018) |
Issue number | 4 |
DOIs | |
Publication status | Published - 2017 |
Keywords
- Large-scale 1D distributed systems
- rank minimization problem
- two-layer Toeplitz structure