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A robust control of a class of induction motors using rough type-2 fuzzy neural networks

  • Mohammad Hosein Sabzalian
  • Ardashir Mohammadzadeh
  • Shuyi Lin
  • Weidong ZhangEmail author
Methodologies and Application
  • 15 Downloads

Abstract

In this paper, a new adaptive control method is presented for a class of induction motors. The dynamics of the system are assumed to be unknown and also are perturbed by some disturbances such as variation of load torque and rotor resistance. A type-2 fuzzy system based on rough neural network (T2FRNN) is proposed to estimate uncertainties. The parameters of T2FRNN are adjusted based on the adaptation laws which are obtained from Lyaponuv stability analysis. The effects of the uncertainties and the approximation errors are compensated by the proposed control method. Simulation results verify the good performance of the proposed control method. Also a numerical comparison is provided to show the effectiveness of the proposed fuzzy system.

Keywords

Induction motor Rough neural network Type-2 fuzzy systems Robust stability analysis Faulty conditions 

Notes

Acknowledgements

This paper is partly supported by the National Science Foundation of China (61473183, U1509211, 61627810), and National Key R&D Program of China (2017YFE0128500).

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Electrical EngineeringUniversity of BonabBonabIran
  3. 3.School of Industrial EngineeringPurdue UniversityWest LafayetteUSA

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