Long Prediction Horizon FCS-MPC for Power Converters and Drives
Finite control set model predictive control (FCS-MPC) is a salient control method for power conversion systems that has recently enjoyed remarkable popularity. Several studies highlight the performance benefits that long prediction horizons achieve in terms of closed-loop stability, harmonic distort...
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| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10115409/ |
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| author | Eduardo Zafra Sergio Vazquez Tobias Geyer Ricardo P. Aguilera Leopoldo G. Franquelo |
| author_facet | Eduardo Zafra Sergio Vazquez Tobias Geyer Ricardo P. Aguilera Leopoldo G. Franquelo |
| author_sort | Eduardo Zafra |
| collection | DOAJ |
| description | Finite control set model predictive control (FCS-MPC) is a salient control method for power conversion systems that has recently enjoyed remarkable popularity. Several studies highlight the performance benefits that long prediction horizons achieve in terms of closed-loop stability, harmonic distortions, and switching losses. However, the practical implementation is not straightforward due to its inherently high computational burden. To overcome this obstacle, the control problem can be formulated as an integer least-squares optimization problem, which is equivalent to the closest point search or closest vector problem in lattices. Different techniques have been proposed in the literature to solve it, with the sphere decoding algorithm (SDA) standing out as the most popular choice to address the long prediction horizon FCS-MPC. However, the state of the art in this field offers solutions beyond the conventional SDA that will be described in this article alongside future trends and challenges in the topic. |
| format | Article |
| id | doaj-art-9b93106f35e442d4b6633ab820bece9f |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-9b93106f35e442d4b6633ab820bece9f2025-01-15T00:03:54ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842023-01-01415917510.1109/OJIES.2023.327289710115409Long Prediction Horizon FCS-MPC for Power Converters and DrivesEduardo Zafra0https://orcid.org/0000-0002-5866-0920Sergio Vazquez1https://orcid.org/0000-0002-7438-8904Tobias Geyer2https://orcid.org/0000-0002-3650-1785Ricardo P. Aguilera3https://orcid.org/0000-0003-4166-8341Leopoldo G. Franquelo4https://orcid.org/0000-0002-1976-9747Electronics Department, Universidad de Sevilla, Sevilla, SpainLaboratory of Engineering for Energy and Environmental Sustainability, Universidad de Sevilla, Sevilla, SpainABB System Drives, Turgi, SwitzerlandSchool of Electrical and Data Engineering, University of Technology Sydney, Broadway, NSW, AustraliaLaboratory of Engineering for Energy and Environmental Sustainability, Universidad de Sevilla, Sevilla, SpainFinite control set model predictive control (FCS-MPC) is a salient control method for power conversion systems that has recently enjoyed remarkable popularity. Several studies highlight the performance benefits that long prediction horizons achieve in terms of closed-loop stability, harmonic distortions, and switching losses. However, the practical implementation is not straightforward due to its inherently high computational burden. To overcome this obstacle, the control problem can be formulated as an integer least-squares optimization problem, which is equivalent to the closest point search or closest vector problem in lattices. Different techniques have been proposed in the literature to solve it, with the sphere decoding algorithm (SDA) standing out as the most popular choice to address the long prediction horizon FCS-MPC. However, the state of the art in this field offers solutions beyond the conventional SDA that will be described in this article alongside future trends and challenges in the topic.https://ieeexplore.ieee.org/document/10115409/Optimization methodsparallel algorithmspower converterspredictive control |
| spellingShingle | Eduardo Zafra Sergio Vazquez Tobias Geyer Ricardo P. Aguilera Leopoldo G. Franquelo Long Prediction Horizon FCS-MPC for Power Converters and Drives IEEE Open Journal of the Industrial Electronics Society Optimization methods parallel algorithms power converters predictive control |
| title | Long Prediction Horizon FCS-MPC for Power Converters and Drives |
| title_full | Long Prediction Horizon FCS-MPC for Power Converters and Drives |
| title_fullStr | Long Prediction Horizon FCS-MPC for Power Converters and Drives |
| title_full_unstemmed | Long Prediction Horizon FCS-MPC for Power Converters and Drives |
| title_short | Long Prediction Horizon FCS-MPC for Power Converters and Drives |
| title_sort | long prediction horizon fcs mpc for power converters and drives |
| topic | Optimization methods parallel algorithms power converters predictive control |
| url | https://ieeexplore.ieee.org/document/10115409/ |
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