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    This study addresses cooperative suboptimal output regulation for heterogeneous multi-agent systems with unknown dynamics and input saturation. Novel distributed control strategies ensure system stability and accurate output tracking, even with incomplete system information.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Networked Systems

    Background:

    • Heterogeneous multi-agent systems present challenges in cooperative control.
    • Input saturation and unknown agent dynamics complicate output regulation.
    • Achieving semiglobal cooperative suboptimal output regulation is a critical problem.

    Purpose of the Study:

    • To develop distributed suboptimal control strategies for heterogeneous multi-agent systems.
    • To address the challenges of unknown agent dynamics and input saturation.
    • To ensure cooperative output regulation and closed-loop stability.

    Main Methods:

    • A model-based approach using low-gain technique and output regulation theory.
    • A data-driven control algorithm for cases with unknown agent dynamics.
    • Distributed control strategy design for cooperative behavior.

    Main Results:

    • Demonstrated successful semiglobal cooperative suboptimal output regulation.
    • Ensured each agent's output tracks the reference signal.
    • Achieved interference suppression and guaranteed closed-loop stability.

    Conclusions:

    • The proposed model-based and data-driven strategies effectively solve the output regulation problem.
    • The control methods guarantee system stability and performance despite uncertainties.
    • Validated theoretical findings through numerical simulations.