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EID-based robust stabilization for delayed fractional-order nonlinear uncertain system with application in memristive neural networks

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  • Yao, Xueqi
  • Zhong, Shouming

Abstract

In this paper, the robust stabilization for fractional-order (FO) delayed nonlinear uncertain system with disturbance is obtained for the first time by using equivalent-input-disturbance (EID) with internal model. And the EID method is applied to FO memristive neural networks (FMNNS) as an application. The fractional-order state-feedback controller is designed, and the gains of controller can be derived by LMI. Three simulations are given, the comparison between EID approach with and without internal model is showed and the observer-based method is compared to emphasize the effectivity of internal model control. And the example for FMNNs and a simple practical example are given to show the accuracy of the proposed results.

Suggested Citation

  • Yao, Xueqi & Zhong, Shouming, 2021. "EID-based robust stabilization for delayed fractional-order nonlinear uncertain system with application in memristive neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000588
    DOI: 10.1016/j.chaos.2021.110705
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    References listed on IDEAS

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    5. Liu, Rui-Juan & Nie, Zhuo-Yun & Wu, Min & She, Jinhua, 2018. "Robust disturbance rejection for uncertain fractional-order systems," Applied Mathematics and Computation, Elsevier, vol. 322(C), pages 79-88.
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    Cited by:

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    2. Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2022. "Fuzzy-weighted differential evolution computing paradigm for fractional order nonlinear wiener systems," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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