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Seeking Tracking Consensus for General Linear Multiagent Systems With Fixed and Switching Signed Networks.

Yuhua Cheng, Lei Shi, Jinliang Shao

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    This study extends tracking consensus models for multiagent systems (MASs) to include competitive relationships. New algebraic conditions ensure consensus even with unstable subsystems in signed networks.

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    Area of Science:

    • Control Theory
    • Network Science
    • Artificial Intelligence

    Background:

    • Existing multiagent systems (MASs) consensus studies are limited to cooperative networks.
    • Real-world MASs, like biological and social systems, often involve competitive interactions.
    • Traditional models fail to capture consensus dynamics in networks with mixed relationships.

    Purpose of the Study:

    • To extend tracking consensus dynamics to signed networks with both cooperative and competitive agents.
    • To analyze consensus in fixed and switching network topologies.
    • To establish conditions for achieving tracking consensus in complex MASs.

    Main Methods:

    • Consideration of a group of agents with general linear dynamics.
    • Analysis of both fixed and switching network structures.
    • Derivation of algebraic conditions based on network topology and edge weights.

    Main Results:

    • Developed a framework for tracking consensus in signed networks.
    • Identified algebraic conditions ensuring consensus implementation.
    • Demonstrated that subsystems can be strictly unstable under specific eigenvalue constraints.

    Conclusions:

    • Tracking consensus is achievable in MASs with mixed cooperative and competitive relationships.
    • Network structure and edge weights are critical for achieving consensus.
    • The model accommodates system instability within defined bounds, broadening applicability.