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Fully Distributed Adaptive NN-Based Consensus Protocol for Nonlinear MASs: An Attack-Free Approach.

Yuezu Lv, Jialing Zhou, Guanghui Wen

    IEEE Transactions on Neural Networks and Learning Systems
    |December 22, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel attack-free consensus protocols for nonlinear multiagent systems (MASs). These adaptive protocols enhance security by forbidding observer information exchange, reducing communication load.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Artificial Intelligence

    Background:

    • Consensus problems in nonlinear multiagent systems (MASs) are critical for coordinated behavior.
    • Existing methods often require extensive information exchange, posing security and communication challenges.
    • Ensuring attack-free operation in distributed systems under directed graphs is a significant concern.

    Purpose of the Study:

    • To design fully distributed adaptive attack-free consensus protocols for nonlinear MASs.
    • To eliminate observer information transmission via communication channels during consensus seeking.
    • To enhance system security and reduce communication burden.

    Main Methods:

    • Utilized a fixed-time observer to estimate local states and consensus errors using local and relative output measurements.
    • Developed an observer-based protocol with adaptive gains to estimate unknown neural network weights and residual error bounds.
    • Introduced an additional adaptive gain to estimate communication connectivity, ensuring a fully distributed and attack-free design.

    Main Results:

    • Successfully designed adaptive, attack-free consensus protocols for nonlinear MASs under directed graphs.
    • Demonstrated that the proposed protocols prevent observer information exchange, ensuring inherent security.
    • Achieved reduced communication load due to the attack-free design structure.

    Conclusions:

    • The developed protocols provide a robust solution for achieving consensus in nonlinear MASs while maintaining attack-free communication.
    • The adaptive nature of the gains allows for estimation of unknown system parameters and connectivity.
    • This approach offers a promising direction for secure and efficient distributed coordination in networked systems.