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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
Institut
Start Page
942
End Page
947
Citation
International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM 2022)
Contribution to Conference
Publisher DOI
Scopus ID
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