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arXiv · 2408.07918

Stable State Space SubSpace (S$^5$) Identification

Xinhui Rong·Victor Solo
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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.

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Xinhui Rong, Victor Solo. 2024-08-15. Stable State Space SubSpace (S$^5$) Identification. https://arxiv.org/abs/2408.07918

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