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    This study introduces event-triggered output feedback for multi-agent systems (MASs) to improve communication efficiency. A modified genetic algorithm (MGA) optimizes control for cluster consensus in MASs, demonstrated with satellite formations.

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    Area of Science:

    • Control Systems Engineering
    • Distributed Systems
    • Robotics

    Background:

    • Multi-agent systems (MASs) often face challenges with limited communication resources.
    • Achieving cluster consensus requires efficient coordination strategies within and between agent groups.
    • Existing methods may not adequately address resource constraints in complex MAS tasks.

    Purpose of the Study:

    • To develop an event-triggered output feedback control strategy for leader-following MASs.
    • To reduce communication load by implementing distinct triggering schemes for intracluster and intercluster communications.
    • To ensure asymptotic stability of the MAS under the proposed control protocol.

    Main Methods:

    • Design of two novel sampled-data-based event-triggered schemes for intra- and intercluster communication.
    • Proposal of an event-based cluster consensus control protocol.
    • Derivation of stability criteria using matrix inequalities.
    • Development of a modified genetic algorithm (MGA) with multiple subpopulations to solve nonlinear, nonconvex controller design criteria.

    Main Results:

    • Sufficient criteria for asymptotic stability of the closed-loop MAS were derived.
    • The MGA effectively addressed the nonlinear and nonconvex nature of the controller design parameters.
    • The proposed event-triggered control significantly alleviates communication resource consumption.
    • The effectiveness was demonstrated through a satellite formation flying example.

    Conclusions:

    • The developed event-triggered output feedback control strategy enhances communication efficiency in MASs.
    • The MGA provides a robust method for optimizing control parameters in complex MAS consensus problems.
    • The findings are applicable to real-world scenarios like satellite formation flying, optimizing resource usage.