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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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