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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Second-order global consensus in multiagent networks with random directional link failure.

Huaqing Li, Xiaofeng Liao, Tingwen Huang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 6, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study establishes conditions for second-order nonlinear consensus in multiagent systems with random network failures. It ensures reliable coordination despite unpredictable link disruptions using Lyapunov functions.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Dynamical Systems

    Background:

    • Achieving consensus in multiagent systems is crucial for coordinated behavior.
    • Previous research often assumes reliable network connections, limiting applicability.
    • Random interconnection failures in directed networks pose significant challenges to consensus.

    Purpose of the Study:

    • To investigate second-order globally nonlinear consensus in multiagent networks with general directed topology.
    • To address the impact of random interconnection failures on system consensus.
    • To develop theoretical criteria for achieving consensus under stochastic conditions.

    Main Methods:

    • Characterizing the behavior of the stochastic dynamical system using its time-averaged counterpart.
    • Deriving a criterion for second-order consensus by constructing a Lyapunov function for the time-averaged network.
    • Establishing a sufficient condition by associating the random switching nonlinear system's solution with the Lyapunov function.

    Main Results:

    • A criterion for second-order consensus is derived based on the time-averaged network.
    • A sufficient condition for consensus is established for networks with random directed interconnections.
    • Consensus requires achievement in the time-averaged network and Lyapunov function decrease along specific system solution subsequences.

    Conclusions:

    • The derived conditions ensure second-order globally nonlinear consensus despite random network failures.
    • The approach provides a robust framework for analyzing consensus in stochastic multiagent systems.
    • Theoretical results are validated through a numerical example, confirming their correctness.