Adaptive Neural Consensus for Fractional-Order Multi-Agent Systems With Faults and Delays

Xiongliang Zhang, Shiqi Zheng, Choon Ki Ahn, Yuanlong Xie

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

This article investigates the consensus control for a class of fractional-order (FO) nonlinear multi-agent systems (MASs). Severe sensor/actuator faults and time-varying delays are both considered in the FO MASs. The severe faults may cause unknown control directions in MASs. A new adaptive controller, which is composed of a distributed FO Nussbaum gain, an FO filter, and an auxiliary function, is presented to deal with the severe faults. To cope with the time-varying delays, two different methods are proposed based on barrier Lyapunov function and Lyapunov-Krasovskii function, respectively. Meanwhile, the radial basis function neural network (RBF NN) is applied to approximate the unknown nonlinear functions during the design procedures. This can result in a low-complexity controller. Finally, two simulation examples are used to verify the validity of the proposed schemes.

Original languageEnglish
Pages (from-to)7873-7886
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume34
Issue number10
DOIs
Publication statusPublished - 2023 Oct 1

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

Keywords

  • Fractional-order (FO)
  • Nussbaum function
  • multi-agent systems (MASs)
  • radial basis function neural network (RBF NN)
  • sensor/actuator faults
  • time-varying delays

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

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