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Disturbance observer-based adaptive neural network FTC for a class of nonlinear MASs with an estimated efficiency factor

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  • Jiyang Jia
  • Jie Lan
  • Yan-Jun Liu
  • Lei Liu

Abstract

An adaptive neural network fault-tolerant control(FTC) scheme is proposed for nonlinear and nonstrict-feedback multi-agent systems (MASs) with directed fixed topology. Firstly, a disturbance observer is designed to estimate the unknown external disturbances in the systems, and realise the dynamic estimation of the disturbances. Secondly, the efficiency factor is estimated online, and then the FTC scheme is designed successfully under the backstepping framework. It is proved that all signals in the closed-loop systems are semi-globally uniformly bounded and the tracking error is controlled in a small range. Finally, an example is given to verify the effectiveness of the proposed method.

Suggested Citation

  • Jiyang Jia & Jie Lan & Yan-Jun Liu & Lei Liu, 2023. "Disturbance observer-based adaptive neural network FTC for a class of nonlinear MASs with an estimated efficiency factor," International Journal of Systems Science, Taylor & Francis Journals, vol. 54(4), pages 751-767, March.
    • Handle: RePEc:taf:tsysxx:v:54:y:2023:i:4:p:751-767
    DOI: 10.1080/00207721.2022.2141596
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