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Intelligence Approaches Based Direct Torque Control of Induction Motor

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Adaptive and Natural Computing Algorithms (ICANNGA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7824))

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Abstract

This paper presents a comparative study of two intelligent techniques to replace conventional comparators and selection table of direct torque control for induction machines, namely fuzzy logic and artificial neural network. The comparison with the conventional direct torque control proves that FL-DTC and NN-DTC reduces the electromagnetic torque ripple, stator flux, and stator current. Simulation results prove the effectiveness and the performances proposed strategies.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Mohammadia Engineering School, Avenue Ibn Sina, B.P.765, Agdal-Rabat, Morocco

    Moulay Rachid Douiri & Mohamed Cherkaoui

Authors
  1. Moulay Rachid Douiri
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  2. Mohamed Cherkaoui
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Editor information

Editors and Affiliations

  1. Départment des Systémes d’Information, Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 105, Lausanne, Switzerland

    Marco Tomassini , Alberto Antonioni , Fabio Daolio  & Pierre Buesser , ,  & 

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Douiri, M.R., Cherkaoui, M. (2013). Intelligence Approaches Based Direct Torque Control of Induction Motor. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-37213-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37212-4

  • Online ISBN: 978-3-642-37213-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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