Deadbeat Direct Flux Vector Control of Surface Permanent Magnet Motor Drives

The predictive control algorithms for electrical drives are currently subject to considerable interest and development. In particular, the predictive torque control is a competitive solution that may replace in the future the conventional control schemes based on linear controllers. Therefore, the p...

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Veröffentlicht in:IEEE transactions on industry applications Jg. 56; H. 3; S. 1
Hauptverfasser: Rubino, S., Bojoi, R., Armando, E., Tenconi, A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Control algorithms
Controllability
Couplings
deadbeat control
direct flux vector control
Directional control
Flux
Inverters
Magnetism
Mathematical model
model predictive control
permanent magnet motor drive
Permanent magnets
Predictive control
Regulation
Rotors
Stability
Stators
Torque
ISSN:0093-9994, 1939-9367
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Zusammenfassung:The predictive control algorithms for electrical drives are currently subject to considerable interest and development. In particular, the predictive torque control is a competitive solution that may replace in the future the conventional control schemes based on linear controllers. Therefore, the paper proposes a predictive torque control for surface permanent magnet motor drives requiring a wide constant power range. The predictive algorithm uses a deadbeat direct flux vector control approach for the simultaneous control of the stator flux amplitude and torque-producing current component. The proposed control scheme is suitable for applications requiring a wide speed range with current and voltage constraints. The proposed deadbeat torque controller has been tested on a fractional slot permanent magnet machine and the experimental results demonstrate the drive full controllability, including deep flux-weakening operation with limitation of the load angle.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2020.2972835