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    This study introduces a new control method for multiagent systems with input constraints, simplifying complex tracking control problems. The approach ensures follower agents synchronize with a dynamic leader, achieving bounded synchronization errors.

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    Area of Science:

    • Control Theory
    • Robotics
    • Systems Engineering

    Background:

    • Multiagent systems often face challenges with input constraints and dynamic leaders.
    • Cooperative tracking control is crucial for coordinated multiagent behaviors.
    • Existing methods can be computationally intensive and complex to implement.

    Purpose of the Study:

    • To develop a novel cooperative tracking control strategy for input-constrained multiagent systems with a dynamic leader.
    • To simplify the control design and computation for high-order nonlinear systems.
    • To ensure robust synchronization and boundedness of system states.

    Main Methods:

    • A system transformation method is proposed to convert input-constrained control into unconstrained output feedback control.
    • The transformed system dynamics are simplified into Brunovsky normal form.
    • The problem is reduced to stabilizing the transformed system, obviating the need for complex backstepping schemes.

    Main Results:

    • Follower agents achieve synchronization with the dynamic leader within bounded errors.
    • All signals within the closed-loop system are proven to be semi-global uniformly ultimately bounded.
    • Numerical analysis validates the theoretical findings and demonstrates the approach's effectiveness.

    Conclusions:

    • The proposed method offers a significantly simplified and effective approach to cooperative tracking control for complex multiagent systems.
    • The technique successfully addresses input constraints and dynamic leader scenarios.
    • This research contributes to advancing the field of coordinated control in multiagent systems.