Stable State Space SubSpace (S$^5$) Identification
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914913609318400 |
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| author | Rong, Xinhui Solo, Victor |
| author_facet | Rong, Xinhui Solo, Victor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07918 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stable State Space SubSpace (S$^5$) Identification Rong, Xinhui Solo, Victor Systems and Control 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. |
| title | Stable State Space SubSpace (S$^5$) Identification |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2408.07918 |