Identification of a nonlinear PMSM model using symbolic regression and its application to current optimization scenarios

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@InProceedings{Bramerdorfer:2014:IECON,
  author =       "Gerd Bramerdorfer and Wolfgang Amrhein and 
                 Stephan M. Winkler and Michael Affenzeller",
  booktitle =    "40th Annual Conference of the IEEE Industrial
                 Electronics Society, IECON 2014",
  title =        "Identification of a nonlinear PMSM model using
                 symbolic regression and its application to current
                 optimization scenarios",
  year =         "2014",
  month =        oct,
  pages =        "628--633",
  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.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IECON.2014.7048566",
  notes =        "Also known as \cite{7048566}",
}

Genetic Programming entries for Gerd Bramerdorfer Wolfgang Amrhein Stephan M Winkler Michael Affenzeller

Citations