Stable State Space SubSpace (S$^5$) Identification
<|reference_start|>Stable State Space SubSpace (S$^5$) Identification: 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.<|reference_end|>
@article{rong2024stable,
    title={Stable State Space SubSpace (S$^5$) Identification},
    author={Xinhui Rong and Victor Solo},
    journal={arXiv preprint arXiv:2408.07918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07918},
    primaryClass={eess.SY cs.SY}
}