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Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems

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  • Jing Bai
  • Yongguang Yu

Abstract

Due to the excellent approximation ability, the neural networks based control method is used to achieve adaptive consensus of the fractional-order uncertain nonlinear multiagent systems with external disturbance. The unknown nonlinear term and the external disturbance term in the systems are compensated by using the radial basis function neural networks method, a corresponding fractional-order adaption law is designed to approach the ideal neural network weight matrix of the unknown nonlinear terms, and a control law is designed eventually. According to the designed Lyapunov candidate function and the fractional theory, the systems stability is proved, and the adaptive consensus can be guaranteed by using the designed control law. Finally, two simulations are shown to illustrate the validity of the obtained results.

Suggested Citation

  • Jing Bai & Yongguang Yu, 2018. "Neural Networks Based Adaptive Consensus for a Class of Fractional-Order Uncertain Nonlinear Multiagent Systems," Complexity, Hindawi, vol. 2018, pages 1-10, November.
    • Handle: RePEc:hin:complx:9014787
    DOI: 10.1155/2018/9014787
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    References listed on IDEAS

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    1. Jing Bai & Guoguang Wen & Ahmed Rahmani & Yongguang Yu, 2015. "Distributed formation control of fractional-order multi-agent systems with absolute damping and communication delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(13), pages 2380-2392, October.
    2. Tiedong Ma & Teng Li & Bing Cui, 2018. "Coordination of fractional-order nonlinear multi-agent systems via distributed impulsive control," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 1-14, January.
    3. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    4. Lin Zhao & Yingmin Jia, 2016. "Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(8), pages 1931-1942, June.
    5. Fei Wang & Yongqing Yang, 2017. "Leader-following consensus of nonlinear fractional-order multi-agent systems via event-triggered control," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(3), pages 571-577, February.
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