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    Area of Science:

    • Control Theory
    • Networked Systems
    • Robotics

    Background:

    • Natural systems demonstrate coordinated group behavior despite individual competition.
    • Multiagent systems (MASs) require robust control strategies for cooperative tasks.
    • Asynchronous communication and cooperation-competition dynamics pose challenges in MAS control.

    Purpose of the Study:

    • To investigate cooperative consensus tracking control for MASs with asynchronous communications.
    • To address challenges posed by cooperation-competition networks in MASs.
    • To develop a flexible control strategy that accounts for individual agent interactions.

    Main Methods:

    • Designed a novel distance-based weight function to model cooperation and competition.
    • Employed convergence analysis of infinite products of super-stochastic matrices.
    • Developed a consensus tracking control scheme for MASs.

    Main Results:

    • Achieved cooperative consensus tracking for all followers to a leader agent.
    • Demonstrated effective control despite competitive interactions and asynchronous communication.
    • Validated the proposed scheme's effectiveness through numerical simulations.

    Conclusions:

    • The proposed control scheme enables cooperative consensus tracking in MASs with complex interaction networks.
    • The novel weight function offers greater flexibility than fixed values in existing methods.
    • The theoretical conditions derived ensure reliable coordinated behavior in MASs.