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Some Aspects of Neural Network State Variable Estimator Improvement in Induction Motor Drive

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

Some aspects of state variable estimator improvement is proposed in the paper. The estimator approximates stator current components in the rotor flux reference frame with the help of neural networks. Some modification of the training procedure is considered that leads to the estimator accuracy improvement. Provided tests confirmed this feature but further steps are necessary to increase state variables estimation in the low supplying frequency range.

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Author information

Authors and Affiliations

  1. Czestochowa University of Technology, Dabrowskiego 69, 42-201, Poland

    Jerzy Jelonkiewicz & Łukasz Laskowski

Authors
  1. Jerzy Jelonkiewicz
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  2. Łukasz Laskowski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2013 Springer-Verlag Berlin Heidelberg

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Jelonkiewicz, J., Laskowski, Ł. (2013). Some Aspects of Neural Network State Variable Estimator Improvement in Induction Motor Drive. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_8

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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