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Disturbance rejection of fractional-order T-S fuzzy neural networks based on quantized dynamic output feedback controller

Author

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  • Karthick, S.A.
  • Sakthivel, R.
  • Ma, Y.K.
  • Mohanapriya, S.
  • Leelamani, A.

Abstract

The disturbance rejection approach is employed for the stabilization of fractional-order neural networks described by Takagi-Sugeno fuzzy model with dynamic output feedback controller under quantization. First, an equivalent continuous frequency distributed integral-order system is formulated for the fractional-order neural networks to estimate the system state. Specifically, the dynamic output feedback control with quantization is proposed and the measurement output is quantized by logarithmic quantizer before transmission. By employing an indirect Lyapunov approach and equivalent input disturbance (EID) technique, a set of newly established sufficient conditions with corresponding quantizer’s dynamic parameters is obtained in the shape of LMIs to ensure the asymptotical stability of the considered fractional-order system. Finally, the validity of the considered design method is illustrated through a numerical example.

Suggested Citation

  • Karthick, S.A. & Sakthivel, R. & Ma, Y.K. & Mohanapriya, S. & Leelamani, A., 2019. "Disturbance rejection of fractional-order T-S fuzzy neural networks based on quantized dynamic output feedback controller," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 846-857.
    • Handle: RePEc:eee:apmaco:v:361:y:2019:i:c:p:846-857
    DOI: 10.1016/j.amc.2019.06.029
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    References listed on IDEAS

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    1. Chang, Xiao-Heng & Li, Zhi-Min & Xiong, Jun & Wang, Yi-Ming, 2017. "LMI approaches to input and output quantized feedback stabilization of linear systems," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 162-175.
    2. Hai Zhang & Renyu Ye & Song Liu & Jinde Cao & Ahmad Alsaedi & Xiaodi Li, 2018. "LMI-based approach to stability analysis for fractional-order neural networks with discrete and distributed delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(3), pages 537-545, February.
    3. Gao, Fang & Wu, Min & She, Jinhua & Cao, Weihua, 2016. "Disturbance rejection in nonlinear systems based on equivalent-input-disturbance approach," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 244-253.
    4. R. Sakthivel & R. Raja & S. M. Anthoni, 2013. "Exponential Stability for Delayed Stochastic Bidirectional Associative Memory Neural Networks with Markovian Jumping and Impulses," Journal of Optimization Theory and Applications, Springer, vol. 158(1), pages 251-273, July.
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