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Sliding mode synchronization controller design with neural network for uncertain chaotic systems

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  • Mou, Chen
  • Jiang, Chang-sheng
  • Bin, Jiang
  • Wu, Qing-xian

Abstract

A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

Suggested Citation

  • Mou, Chen & Jiang, Chang-sheng & Bin, Jiang & Wu, Qing-xian, 2009. "Sliding mode synchronization controller design with neural network for uncertain chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 39(4), pages 1856-1863.
    • Handle: RePEc:eee:chsofr:v:39:y:2009:i:4:p:1856-1863
    DOI: 10.1016/j.chaos.2007.06.113
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    Cited by:

    1. Zhang, Qunjiao, 2014. "Robust synchronization of FitzHugh–Nagumo network with parameter disturbances by sliding mode control," Chaos, Solitons & Fractals, Elsevier, vol. 58(C), pages 22-26.
    2. Chen, Mou & Chen, Wen-hua, 2009. "Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2716-2724.
    3. Yao, Qijia, 2021. "Neural adaptive learning synchronization of second-order uncertain chaotic systems with prescribed performance guarantees," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Jin, Maolin & Chang, Pyung Hun, 2009. "Simple robust technique using time delay estimation for the control and synchronization of Lorenz systems," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2672-2680.

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