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  4. Data-Driven Model Predictive Current Control for Synchronous Machines: a Koopman Operator Approach
 
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Data-Driven Model Predictive Current Control for Synchronous Machines: a Koopman Operator Approach

Publikationstyp
Conference Paper
Date Issued
2022-06
Sprache
English
Author(s)
Martínez Calderón, Horacio  
Hammoud, Issa  
Oehlschlägel, Thimo  
Werner, Herbert  
Kennel, Ralph  
Institut
Regelungstechnik E-14  
TORE-URI
http://hdl.handle.net/11420/13607
Start Page
942
End Page
947
Citation
International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM 2022)
Contribution to Conference
International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2022  
Publisher DOI
10.1109/SPEEDAM53979.2022.9842164
Scopus ID
2-s2.0-85136219150
In this paper, a data-driven continuous control set model predictive current control (CCS-MPCC) scheme for permanent magnet synchronous motors (PMSMs) is proposed. The model of the motor used in the model predictive control (MPC) strategy is obtained from collected measurements using the Koopman operator (KO) theory. Experimental results on a 500W PMSM show that the obtained model has yielded excellent prediction accuracy, and that it is capable of being incorporated within a real-time CCS-MPCC scheme in the sub-millisecond typically available sampling time for the current control loop of synchronous motors.
Subjects
continuous control set model predictive control
data-driven modelling
Koopman operator theory
online optimization
Synchronous machines
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