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Titel:

Deep Learning-Based Long-Horizon MPC: Robust, High Performing, and Computationally Efficient Control for PMSM Drives

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Mohammad Abu-Ali; Felix Berkel; Maximilian Manderla; Sven Reimann; Ralph Kennel; Mohamed Abdelrahem
Abstract:
This article presents a computationally efficient and high performing approximate long-horizon model predictive control (MPC) for permanent magnet synchronous motors (PMSMs). Two continuous control set MPC (CCS-MPC) formulations are considered: the classical current tracking delta MPC (Del-MPC) and the torque tracking economic MPC (EMPC). To achieve offset-free torque tracking under model uncertainties and in all regions of operation, a disturbance observer and a dq-current reference generator a...     »
Stichworte:
Torque, Real-time systems, Generators, Predictive models, Neural networks, Deep learning, Cost function
Zeitschriftentitel:
IEEE Transactions on Power Electronics
Jahr:
2022
Jahr / Monat:
2022-05
Quartal:
2. Quartal
Monat:
May
Heft / Issue:
Volume: 37, Issue: 10, Oct. 2022
Seitenangaben Beitrag:
12486 - 12501
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/TPEL.2022.3172681
Verlag / Institution:
IEEE
Print-ISSN:
0885-8993
E-ISSN:
1941-0107
Publikationsdatum:
05.05.2022
Semester:
SS 22
TUM Einrichtung:
Lehrstuhl für Hochleistungs-Umrichtersysteme
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