dc.creator | Zafra Ratia, Eduardo | es |
dc.creator | Vázquez Pérez, Sergio | es |
dc.creator | Geyer, Tobias | es |
dc.creator | Aguilera, Ricardo P. | es |
dc.creator | García Franquelo, Leopoldo | es |
dc.date.accessioned | 2023-08-10T11:05:05Z | |
dc.date.available | 2023-08-10T11:05:05Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Zafra Ratia, E., Vázquez Pérez, S., Geyer, T., Aguilera, R.P. y García Franquelo, L. (2023). Long Prediction Horizon FCS-MPC for Power Converters and Drives. IEEE Open Journal of the Industrial Electronics Society, 4, 159-175. https://doi.org/10.1109/OJIES.2023.3272897. | |
dc.identifier.issn | 2644-1284 | es |
dc.identifier.uri | https://hdl.handle.net/11441/148435 | |
dc.description | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | es |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | IEEE Open Journal of the Industrial Electronics Society, 4, 159-175. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Optimization methods | es |
dc.subject | Parallel algorithms | es |
dc.subject | Power converters | es |
dc.subject | Predictive control | es |
dc.title | Long Prediction Horizon FCS-MPC for Power Converters and Drives | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | es |
dc.relation.projectID | PID2020-115561RB-C31 | es |
dc.relation.projectID | TED2021-130613B-I00 | es |
dc.relation.projectID | FPU18/02704 | es |
dc.relation.projectID | DP210101382 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/10115409 | es |
dc.identifier.doi | 10.1109/OJIES.2023.3272897 | es |
dc.contributor.group | Universidad de Sevilla. TIC109: Tecnología Electrónica | es |
dc.journaltitle | IEEE Open Journal of the Industrial Electronics Society | es |
dc.publication.volumen | 4 | es |
dc.publication.initialPage | 159 | es |
dc.publication.endPage | 175 | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Consejo de Investigación. Australia | es |