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Consensus of Multiagent Systems With Distance-Dependent Communication Networks.

Gangshan Jing1, Yuanshi Zheng1, Long Wang2

  • 1Center for Complex Systems, School of Mechano-Electronic Engineering, Xidian University, Xi'an, China.

IEEE Transactions on Neural Networks and Learning Systems
|January 24, 2017
PubMed
Summary
This summary is machine-generated.

This study explores multiagent systems reaching consensus in networks where communication strength depends on distance. Consensus is achievable in fixed and distance-dependent networks under specific conditions, with applications in opinion dynamics and formation control.

Keywords:
Communication networksLaplace equationsMulti-agent systemsRobot sensing systemsSwitchesSymmetric matrices

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Area of Science:

  • Control Theory
  • Network Science
  • Distributed Systems

Background:

  • Multiagent systems require coordination for collective tasks.
  • Communication network topology significantly impacts system consensus.
  • Distance-dependent communication introduces complexities in analyzing consensus.

Purpose of the Study:

  • To investigate consensus problems in discrete-time and continuous-time multiagent systems.
  • To analyze systems with distance-dependent communication weights and connectivity.
  • To explore applications in opinion consensus and formation control.

Main Methods:

  • Analysis of fixed connectivity networks with nonincreasing communication weights.
  • Study of distance-dependent connectivity networks based on agent proximity.
  • Mathematical proofs for consensus achievement under defined conditions.

Main Results:

  • Consensus is reachable in fixed networks if the communication weight decay rate is bounded.
  • Asymptotic consensus is proven for distance-dependent networks with specific initial graph properties.
  • Theoretical findings are validated through simulation examples.

Conclusions:

  • The decay rate of communication weights is crucial for achieving consensus in fixed networks.
  • Network connectivity range and initial graph properties determine consensus in distance-dependent systems.
  • The developed framework is applicable to real-world problems like opinion formation and coordinated movement.