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Modified projective synchronization of distributive fractional order complex dynamic networks with model uncertainty via adaptive control

Author

Listed:
  • Aadhithiyan, S.
  • Raja, R.
  • Zhu, Q.
  • Alzabut, J.
  • Niezabitowski, M.
  • Lim, C.P.

Abstract

Adaptive modified projective function synchronization of non-linear distributive fractional order complex dynamical networks (DFCDN) is addressed in this paper. Firstly, we have created a model for DFCDN with model uncertainty, external disturbances and uncertain parameters. Based on the Laplace and inverse Laplace transform property of distributive order fractional differential equations and Lyapunov stability theory, we realize the projective synchronization between the DFCDN system, according to the given scaling function by using the novel adaptive controller that we have constructed. Finally two numerical examples and simulations are given to show our proposed work is more realistic and more effective than the existing ones in the literature.

Suggested Citation

  • Aadhithiyan, S. & Raja, R. & Zhu, Q. & Alzabut, J. & Niezabitowski, M. & Lim, C.P., 2021. "Modified projective synchronization of distributive fractional order complex dynamic networks with model uncertainty via adaptive control," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:chsofr:v:147:y:2021:i:c:s096007792100206x
    DOI: 10.1016/j.chaos.2021.110853
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    References listed on IDEAS

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    Cited by:

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    2. Lai, Qiang & Chen, Zhijie, 2023. "Dynamical analysis and finite-time synchronization of grid-scroll memristive chaotic system without equilibrium," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Wang, Xue & Luo, Danfeng & Zhu, Quanxin, 2022. "Ulam-Hyers stability of caputo type fuzzy fractional differential equations with time-delays," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    4. Kiruthika, R. & Krishnasamy, R. & Lakshmanan, S. & Prakash, M. & Manivannan, A., 2023. "Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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