Non-asymptotic Error Analysis of Subspace Identification for Deterministic Systems
Abstract
The subspace identification method (SIM) has been extensively employed in the identification of discrete-time multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems. This paper focuses on the analysis of perturbation errors for the system matrices in state-space models and the corresponding system poles, under two unified SIMs, based on a single finite-length input/output sample trajectory. Specifically, we derive non-asymptotic upper bounds on these errors, providing a unified perspective across various SIM variants. Furthermore, we prove that SIMs become ill-conditioned for MIMO systems when the state-to-output dimensionality ratio is large, regardless of system parameters. Finally, numerical experiments are conducted to validate the non-asymptotic results and the ill-conditionedness of SIMs.
Cite
@article{arxiv.2412.16761,
title = {Non-asymptotic Error Analysis of Subspace Identification for Deterministic Systems},
author = {Shuai Sun},
journal= {arXiv preprint arXiv:2412.16761},
year = {2025}
}
Comments
In Assumption 1, the assumption regarding the matrix A is found to be unreasonable and needs correction. Additionally, the bound curve in Figure 3b is inaccurate and requires revision. To address these issues, I am requesting the withdrawal of this version (v2) and plan to re-upload a corrected version