Combined current control and system identification of a PMSM with neural ordinary differential equations / by Marvin Meyer ; Supervisor: Wilhelm Kirchgässner and Maximilian Schenke

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Title
Combined current control and system identification of a PMSM with neural ordinary differential equations : / by Marvin Meyer ; Supervisor: Wilhelm Kirchgässner and Maximilian Schenke
Author
Meyer, Marvin
Participants
Wallscheid, Oliver ; Schenke, Maximilian
Published
Paderborn, 2022
Description
1 Online-Ressource (iv, 76 Seiten) : Illustrationen, Diagramme
Institutional Note
Universität Paderborn, Masterarbeit, 2022
Annotation
Tag der Abgabe: 24.11.2022
Date of Submission
24/11/2022
Language
English
Document Types
Master Thesis
URN
urn:nbn:de:hbz:466:2-44183
DOI
10.17619/UNIPB/1-1656

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Abstract

This thesis deals with current control and system identification of a permanent magnet synchronous machine. To learn a data-driven controller for current control, a model of the motor is required. Such model can be constructed with the utilization of neural ordinary differential equations. Through this approach, expert knowledge can be used in combination with learnable parameters to identify the motor parameters. With the help of this model, a neural network can then be trained to control the current. The proposed approach will be compared with both model-based and model-free methods for current control.

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