Performance Adjustable Event-Triggered Synchronization Policies to Nonlinear Multiagent Systems

Ming Zhe Dai, Choon Ki Ahn, Jin Wu, Chengxi Zhang, Mingzhen Gui

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

This article designs performance adjustable event- and self-triggered policies for nonlinear multiagent systems with controller output fluctuations. In the provided event-triggered mechanism, a performance adjustable function is introduced, which can freely adjust the system sampling frequencies and state convergence rates. In the proposed self-triggered policy, continuous measurement error monitoring and neighbors’ event listening are avoided, thereby further saving system sensor resources. Furthermore, under a unified framework, the controllers’ inherent nonlinearities and the influence of data quantization in digital communication networks are studied. In comparison with the previous event-triggered sampling schemes, the developed policies do not only study the multiagent coordinated control in terms of system sampling performance and state convergence performance but are also suitable for the scenarios with controller output fluctuations. Numerical simulation results verify the effectiveness and superiority of the proposed policies.

Original languageEnglish
JournalIEEE Systems Journal
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Controller output fluctuation
  • Convergence
  • Eigenvalues and eigenfunctions
  • Laplace equations
  • Multi-agent systems
  • Nonlinear dynamical systems
  • Quantization (signal)
  • System performance
  • event-triggered policies
  • multiagent coordinated control
  • performance adjustable

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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