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Transmission-Constrained Consensus Over Random Graphs.

Xiaotian Wang, Housheng Su

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    |April 7, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a new model for consensus in multiagent systems facing information distortions and random network flow. The research confirms agents reach agreement despite these real-world challenges.

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    Area of Science:

    • Control Theory
    • Network Science
    • Distributed Systems

    Background:

    • Information exchange is vital for agent consensus.
    • Real-world conditions introduce information distortions and stochastic flow.
    • Existing models may not fully capture these transmission constraints.

    Purpose of the Study:

    • To propose a novel model for transmission-constrained consensus over random networks.
    • To analyze the impact of information distortions and stochastic flow on consensus.
    • To provide a theoretical framework for consensus under imperfect information sharing.

    Main Methods:

    • Modeling transmission constraints with heterogeneous functions.
    • Utilizing directed random graphs to represent stochastic information flow.
    • Applying stochastic stability theory and martingale convergence theorem for analysis.

    Main Results:

    • Demonstrated that agent states converge to a consensus value with probability 1.
    • Confirmed consensus is achievable despite information distortions.
    • Validated the model's effectiveness through numerical simulations.

    Conclusions:

    • The proposed model effectively addresses consensus challenges in complex environments.
    • Theoretical analysis and simulations confirm robust consensus achievement.
    • This work offers a valuable framework for understanding consensus in realistic networked systems.