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

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
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Actuators
  • Artificial neural networks
  • Circuit faults
  • Consensus control
  • Delays
  • Fractional-order (FO)
  • multi-agent systems (MASs)
  • Nussbaum function
  • radial basis function neural network (RBF NN)
  • Robot sensing systems
  • sensor/actuator faults
  • time-varying delays.
  • Time-varying systems

ASJC Scopus subject areas

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

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