Learning Stable and Robust Linear Parameter-Varying State-Space Models
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866917572438392832 |
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| author | Verhoek, Chris Wang, Ruigang Tóth, Roland |
| author_facet | Verhoek, Chris Wang, Ruigang Tóth, Roland |
| contents | This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value $γ$. Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01828 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Stable and Robust Linear Parameter-Varying State-Space Models Verhoek, Chris Wang, Ruigang Tóth, Roland Systems and Control Machine Learning This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value $γ$. Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem. |
| title | Learning Stable and Robust Linear Parameter-Varying State-Space Models |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2304.01828 |