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Asynchronous event-based sampling data for impulsive protocol on consensus of non-linear multi-agent systems.

Yiyan Han1, Chuandong Li1, Zhigang Zeng2

  • 1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 13, 2019
PubMed
Summary
This summary is machine-generated.

This study addresses consensus in non-linear multi-agent systems using event-based sampling. It develops methods to reduce system load and ensure consensus, even in complex network topologies.

Keywords:
ConsensusDiscrete-time non-linear multi-agent systemsEvent-based asynchronous sampling dataImpulsive protocol

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Area of Science:

  • Control Theory
  • Networked Systems
  • Robotics

Background:

  • Consensus is crucial for coordinated behavior in multi-agent systems.
  • Traditional methods often require synchronous communication, which is resource-intensive.
  • Asynchronous data sampling presents challenges for achieving consensus.

Purpose of the Study:

  • To develop an impulsive protocol for non-linear multi-agent systems using event-based asynchronously sampled data.
  • To investigate consensus in directed network topologies, both with and without a leader.
  • To reduce the computational load on agents during communication.

Main Methods:

  • Designing an impulsive protocol with event-based asynchronous data sampling.
  • Separating sampling and communication time instants to reallocate resources.
  • Analyzing system stability and consensus conditions for directed networks.
  • Employing numerical simulations to validate the proposed approach.

Main Results:

  • Sufficient conditions for achieving consensus in non-linear multi-agent systems were established.
  • The proposed event-based sampling scheme effectively reduces system load.
  • The method demonstrated effectiveness in both leader-based and leaderless network topologies.
  • Numerical simulations confirmed the theoretical findings.

Conclusions:

  • The developed event-based impulsive protocol ensures consensus in non-linear multi-agent systems.
  • Resource optimization is achieved by decoupling sampling and communication events.
  • The methodology is robust for directed network topologies, with or without a leader.