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A data-driven approach to model-reference control with applications to particle accelerator power converters
A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the $_{\infty}, H_2,...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1016/j.conengprac.2018.10.007 http://cds.cern.ch/record/2816663 |
| _version_ | 1780973611830476800 |
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| author | Nicoletti, Achille Martino, Michele Karimi, Alireza |
| author_facet | Nicoletti, Achille Martino, Michele Karimi, Alireza |
| author_sort | Nicoletti, Achille |
| collection | CERN |
| description | A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the $_{\infty}, H_2, H_1$ or sense) to shape the closed-loop sensitivity functions while guaranteeing the closed-loop stability. The effectiveness of the method is illustrated by considering several case studies where the proposed design scheme is applied in both simulation and to a power converter control system for a specific accelerator requirement at CERN. |
| id | cern-2816663 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2019 |
| record_format | invenio |
| spelling | cern-28166632022-07-26T19:11:50Zdoi:10.1016/j.conengprac.2018.10.007http://cds.cern.ch/record/2816663engNicoletti, AchilleMartino, MicheleKarimi, AlirezaA data-driven approach to model-reference control with applications to particle accelerator power convertersAccelerators and Storage RingsDetectors and Experimental TechniquesA new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the $_{\infty}, H_2, H_1$ or sense) to shape the closed-loop sensitivity functions while guaranteeing the closed-loop stability. The effectiveness of the method is illustrated by considering several case studies where the proposed design scheme is applied in both simulation and to a power converter control system for a specific accelerator requirement at CERN.oai:cds.cern.ch:28166632019 |
| spellingShingle | Accelerators and Storage Rings Detectors and Experimental Techniques Nicoletti, Achille Martino, Michele Karimi, Alireza A data-driven approach to model-reference control with applications to particle accelerator power converters |
| title | A data-driven approach to model-reference control with applications to particle accelerator power converters |
| title_full | A data-driven approach to model-reference control with applications to particle accelerator power converters |
| title_fullStr | A data-driven approach to model-reference control with applications to particle accelerator power converters |
| title_full_unstemmed | A data-driven approach to model-reference control with applications to particle accelerator power converters |
| title_short | A data-driven approach to model-reference control with applications to particle accelerator power converters |
| title_sort | data-driven approach to model-reference control with applications to particle accelerator power converters |
| topic | Accelerators and Storage Rings Detectors and Experimental Techniques |
| url | https://dx.doi.org/10.1016/j.conengprac.2018.10.007 http://cds.cern.ch/record/2816663 |
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