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Modified fuzzy adaptive controller applied to nonlinear systems modeled under quasi-ARX neural network

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Abstract

In this article, a fuzzy adaptive controller approach is presented for nonlinear systems. The proposed quasi-ARX neural network based on Lyapunov learning algorithm is used to update its weight for prediction model as well as to modify fuzzy adaptive controller. The improving performances of the Lyapunov learning algorithm are stable in the learning process of the controller and able to increase the accuracy of the controller as well as fast convergence of error. The simulations are intended to show the effectiveness of the proposed method.

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Acknowledgements

This research has been supported by DIKTI and PPNS.

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Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, Kitakyushu-shi, Fukuoka, 808-0135, Japan

    Imam Sutrisno, Mohammad Abu Jami’in & Jinglu Hu

  2. Politeknik Perkapalan Negeri Surabaya (PPNS), Surabaya, Indonesia

    Imam Sutrisno & Mohammad Abu Jami’in

Authors
  1. Imam Sutrisno
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  2. Mohammad Abu Jami’in
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  3. Jinglu Hu
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Corresponding authors

Correspondence to Imam Sutrisno or Jinglu Hu.

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Sutrisno, I., Jami’in, M.A. & Hu, J. Modified fuzzy adaptive controller applied to nonlinear systems modeled under quasi-ARX neural network. Artif Life Robotics 19, 22–26 (2014). https://doi.org/10.1007/s10015-013-0137-6

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  • DOI: https://doi.org/10.1007/s10015-013-0137-6

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