Neural-Network-Based Predefined-Time Adaptive Consensus in Nonlinear Multi-Agent Systems With Switching Topologies

Yanzheng Zhu, Zuo Wang, Hongjing Liang, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

A predefined-time adaptive consensus control strategy is developed for a class of multi-agent systems containing unknown nonlinearity. The unknown dynamics and switching topologies are simultaneously considered to adapt to actual scenarios. The time required for tracking error convergence can be easily adjusted using the proposed time-varying decay functions. An efficient method is proposed to determine the expected convergence time. Subsequently, the predefined time is adjustable by regulating the parameters of the time-varying functions (TVFs). The neural network (NN) approximation technique is used to address the issue of unknown nonlinear dynamics through predefined-time consensus control. The Lyapunov stability theory testifies that the predefined-time tracking error signals are bounded and convergent. The feasibility and effectiveness of the proposed predefined-time consensus control scheme are demonstrated through the simulation results.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2023

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Artificial neural networks
  • Consensus control
  • Convergence
  • Multi-agent systems
  • Network topology
  • Nonlinear dynamical systems
  • Switches
  • Topology
  • neural-network-based adaptive control
  • predefined-time consensus
  • time-varying functions (TVFs)
  • unknown nonlinear dynamics

ASJC Scopus subject areas

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

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