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Control of Induction Motor Using Artificial Neural Network

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Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 517))

Abstract

The main objective of this paper is to design a controller for control of an Induction motor. In this paper, we have proposed v/f control of induction motor using artificial neural network, the network is trained using back propagation algorithm and Levenberg–Marquardt learning is used faster computation. The main approach is to keep voltage and frequency ratio constant to obtain constant flux over the entire range of operation and thus to have precise control of the machine. The effectiveness of the controller is demonstrated using MATLAB/Simulink simulation.

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Abbreviations

ANN:

Artificial Neural Network

BP:

Back Propagation

PI:

Proportional-Integral

NN:

Neural Network

IM:

Induction motor

VSI:

Voltage Source Inverter

PWM:

Pulse Width Modulation

EMF:

Electromagnetic Force

RPM:

Rotation Per Minute

References

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Authors and Affiliations

  1. VIT University, Vellore, India

    Abhishek Kumar, Rohit Singh, Chandan Singh Mahodi & Sarat Kumar Sahoo

Authors
  1. Abhishek Kumar
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  2. Rohit Singh
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  3. Chandan Singh Mahodi
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  4. Sarat Kumar Sahoo
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Corresponding author

Correspondence to Abhishek Kumar .

Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

  2. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    K. Vijayakumar

  3. Department of Electrical and Electronics Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Bijaya Ketan Panigrahi

  4. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

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Kumar, A., Singh, R., Singh Mahodi, C., Kumar Sahoo, S. (2017). Control of Induction Motor Using Artificial Neural Network. In: Dash, S., Vijayakumar, K., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-10-3174-8_66

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  • DOI: https://doi.org/10.1007/978-981-10-3174-8_66

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3173-1

  • Online ISBN: 978-981-10-3174-8

  • eBook Packages: EngineeringEngineering (R0)

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