Identification of a nonlinear PMSM model using symbolic regression and its application to current optimization scenarios
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- @InProceedings{Bramerdorfer:2014:IECON,
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author = "Gerd Bramerdorfer and Wolfgang Amrhein and
Stephan M. Winkler and Michael Affenzeller",
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booktitle = "40th Annual Conference of the IEEE Industrial
Electronics Society, IECON 2014",
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title = "Identification of a nonlinear PMSM model using
symbolic regression and its application to current
optimization scenarios",
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year = "2014",
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month = oct,
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pages = "628--633",
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abstract = "This article presents the nonlinear modelling of the
torque of brushless PMSMs by using symbolic regression.
It is still popular to characterise the operational
behaviour of electrical machines by employing linear
models. However, nowadays most PMSMs are highly used
and thus a linear motor model does not give an adequate
accuracy for subsequently derived analyses, e.g., for
the calculation of the maximum torque per ampere (MTPA)
trajectory. This article focuses on modelling PMSMs by
nonlinear white-box models derived by symbolic
regression methods. An optimised algebraic equation for
modelling the machine behaviour is derived using
genetic programming. By using a Fourier series
representation of the motor torque a simple to handle
model with high accuracy can be derived. A case study
is provided for a given motor design and the motor
model obtained is used for deriving the MTPA-trajectory
for sinusoidal phase currents. The model is further
applied for determining optimised phase current
waveforms ensuring zero torque ripple.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/IECON.2014.7048566",
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notes = "Also known as \cite{7048566}",
- }
Genetic Programming entries for
Gerd Bramerdorfer
Wolfgang Amrhein
Stephan M Winkler
Michael Affenzeller
Citations