Stable State Space SubSpace (S$^5$) Identification
Systems and Control
2024-08-19 v2 Systems and Control
Abstract
State space subspace algorithms for input-output systems have been widely applied but also have a reasonably well-developedasymptotic theory dealing with consistency. However, guaranteeing the stability of the estimated system matrix is a major issue. Existing stability-guaranteed algorithms are computationally expensive, require several tuning parameters, and scale badly to high state dimensions. Here, we develop a new algorithm that is closed-form and requires no tuning parameters. It is thus computationally cheap and scales easily to high state dimensions. We also prove its consistency under reasonable conditions.
Cite
@article{arxiv.2408.07918,
title = {Stable State Space SubSpace (S$^5$) Identification},
author = {Xinhui Rong and Victor Solo},
journal= {arXiv preprint arXiv:2408.07918},
year = {2024}
}