Artículo
Long Prediction Horizon FCS-MPC for Power Converters and Drives
Autor/es | Zafra Ratia, Eduardo
Vázquez Pérez, Sergio Geyer, Tobias Aguilera, Ricardo P. García Franquelo, Leopoldo |
Departamento | Universidad de Sevilla. Departamento de Ingeniería Electrónica |
Fecha de publicación | 2023 |
Fecha de depósito | 2023-08-10 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Agencia Estatal de Investigación. España Ministerio de Ciencia, Innovación y Universidades (MICINN). España Consejo de Investigación. Australia |
Identificador del proyecto | PID2020-115561RB-C31
TED2021-130613B-I00 FPU18/02704 DP210101382 |
Cita | 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. |
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IES_2023_Zafra_Long_OA.pdf | 1.909Mb | [PDF] | Ver/ | |