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Titel:

Long-Horizon Direct Model Predictive Control Based on Neural Networks for Electrical Drives

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Issa Hammoud; Sebastian Hentzelt; Thimo Oehlschlaegel; Ralph Kennel
Abstract:
In this work, the use of a multilayer perceptron feedforward neural network is proposed to capture the solution of the long-horizon finite control set model predictive control (FCS-MPC) problem in electrical drive systems. The motivation behind this research is based on treating the direct model predictive control problem of a power converter as a multi-class classification problem as it consists of a finite set of switching states, which can be seen as a finite number of different classes. By s...     »
Kongress- / Buchtitel:
Proceedings of IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
Datum der Konferenz:
18-21 Oct. 2020
Verlag / Institution:
IEEE
Publikationsdatum:
18.11.2020
Jahr:
2020
Quartal:
4. Quartal
Jahr / Monat:
2020-11
Monat:
Nov
Seiten:
pp. 3057-3064
Print-ISBN:
978-1-7281-5415-2
E-ISBN:
978-1-7281-5414-5
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/IECON43393.2020.9254388
Semester:
WS 20-21
TUM Einrichtung:
Lehrstuhl für Elektrische Antriebssysteme und Leistungselektronik
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