Online Discrete Optimization of Weighting Factor in Model Predictive Torque and Flux Control of Induction Motor

S. Alireza Davari, Vahab Nekoukar, Shirin Azadi, Freddy Flores, Cristian Garcia, Jose Rodriguez

Producción científica: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

Resumen

Model predictive torque and flux control has shown some advantages over the classical methods. However, one of the challenges that still need to be investigated is the establishment of a control balance between the torque and flux which leads to better switching state selection. Traditionally, a weighting factor is used in the classical model predictive control (MPC). There are some new techniques that tried to avoid using a weighting factor in order to select the optimal switching state. However, in most of them, new optimization problems are added to the method. In this research, a simple discrete optimization technique is proposed for weighting factor and switching state optimization. The proposed method can be applied to the full range of operating points. The simulation and experimental results show the validity of the proposed method.

Idioma originalInglés
Título de la publicación alojadaIECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
EditorialIEEE Computer Society
ISBN (versión digital)9781665480253
DOI
EstadoPublicada - 2022
Evento48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Bélgica
Duración: 17 oct. 202220 oct. 2022

Serie de la publicación

NombreIECON Proceedings (Industrial Electronics Conference)
Volumen2022-October

Conferencia

Conferencia48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
País/TerritorioBélgica
CiudadBrussels
Período17/10/2220/10/22

Áreas temáticas de ASJC Scopus

  • Ingeniería de control y sistemas
  • Ingeniería eléctrica y electrónica

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