Data-driven control and transfer learning using neural canonical control structures*
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_ | 1866929572389322752 |
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| author | Ecker, Lukas Schöberl, Markus |
| author_facet | Ecker, Lukas Schöberl, Markus |
| contents | An indirect data-driven control and transfer learning approach based on a data-driven feedback linearization with neural canonical control structures is proposed. An artificial neural network auto-encoder structure trained on recorded sensor data is used to approximate state and input transformations for the identification of the sampled-data system in Brunovsky canonical form. The identified transformations, together with a designed trajectory controller, can be transferred to a system with varied parameters, where the neural network weights are adapted using newly collected recordings. The proposed approach is demonstrated using an academic and an industrially motivated example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_04042 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven control and transfer learning using neural canonical control structures* Ecker, Lukas Schöberl, Markus Optimization and Control An indirect data-driven control and transfer learning approach based on a data-driven feedback linearization with neural canonical control structures is proposed. An artificial neural network auto-encoder structure trained on recorded sensor data is used to approximate state and input transformations for the identification of the sampled-data system in Brunovsky canonical form. The identified transformations, together with a designed trajectory controller, can be transferred to a system with varied parameters, where the neural network weights are adapted using newly collected recordings. The proposed approach is demonstrated using an academic and an industrially motivated example. |
| title | Data-driven control and transfer learning using neural canonical control structures* |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2302.04042 |