Subspace identification of large-scale interconnected systems

TL;DR

This paper introduces a decentralized subspace identification method for large interconnected systems, leveraging local data and neighborhood approximations to efficiently identify subsystem models.

Contribution

The paper presents a novel decentralized subspace algorithm that efficiently identifies local subsystem models in large-scale interconnected systems using neighborhood-based data.

Findings

Algorithm effectively identifies local subsystem models

Computational feasibility for large-scale systems

Numerical results confirm accuracy and efficiency

Abstract

We propose a decentralized subspace algorithm for identification of large-scale, interconnected systems that are described by sparse (multi) banded state-space matrices. First, we prove that the state of a local subsystem can be approximated by a linear combination of inputs and outputs of the local subsystems that are in its neighborhood. Furthermore, we prove that for interconnected systems with well-conditioned, finite-time observability Gramians (or observability matrices), the size of this neighborhood is relatively small. On the basis of these results, we develop a subspace identification algorithm that identifies a state-space model of a local subsystem from the local input-output data. Consequently, the developed algorithm is computationally feasible for interconnected systems with a large number of local subsystems. Numerical results confirm the effectiveness of the new identification algorithm.