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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Torque, Real-time systems, Generators, Predictive models, Neural networks, Deep learning, Cost function
Journal title:
IEEE Transactions on Power Electronics
Year:
2022
Year / month:
2022-05
Quarter:
2. Quartal
Month:
May
Journal issue:
Volume: 37, Issue: 10, Oct. 2022
Pages contribution:
12486 - 12501
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1109/TPEL.2022.3172681
Publisher:
IEEE
Print-ISSN:
0885-8993
E-ISSN:
1941-0107
Date of publication:
05.05.2022
Semester:
SS 22
TUM Institution:
Lehrstuhl für Hochleistungs-Umrichtersysteme
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