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Neural-network-based speed controller for induction motors using inverse dynamics model

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Abstract.

Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Hassanein S. Ahmed

  2. Department of Power Electronic, Electronic Research Institute, Giza, Egypt

    Hassanein S. Ahmed

  3. Department of Mathematics, Faculty Science, Taibah University, Medina, Saudi Arabia

    Kamel Mohamed

  4. Department of Mathematics, Faculty of Science, New Valley, Assiut University, Assiut, Egypt

    Kamel Mohamed

Authors
  1. Hassanein S. Ahmed
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  2. Kamel Mohamed
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Correspondence to Hassanein S. Ahmed.

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Ahmed, H., Mohamed, K. Neural-network-based speed controller for induction motors using inverse dynamics model. Eur. Phys. J. Plus 131, 292 (2016). https://doi.org/10.1140/epjp/i2016-16292-2

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  • DOI: https://doi.org/10.1140/epjp/i2016-16292-2

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