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

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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.

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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).

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Mohammad Hosein Sabzalian, Shuyi Lin & Weidong Zhang

  2. Department of Electrical Engineering, University of Bonab, Bonab, Iran

    Ardashir Mohammadzadeh

  3. School of Industrial Engineering, Purdue University, West Lafayette, IN, 47906, USA

    Shuyi Lin

Authors
  1. Mohammad Hosein Sabzalian
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  2. Ardashir Mohammadzadeh
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  3. Shuyi Lin
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  4. Weidong Zhang
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Correspondence to Weidong Zhang.

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Communicated by V. Loia.

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Sabzalian, M.H., Mohammadzadeh, A., Lin, S. et al. A robust control of a class of induction motors using rough type-2 fuzzy neural networks. Soft Comput 24, 9809–9819 (2020). https://doi.org/10.1007/s00500-019-04493-3

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