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    This study presents a new control method for multiagent systems to achieve collision-free formations despite unknown dynamics. The approach ensures agents track formation goals while maintaining safe distances, enhancing cooperative control.

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    Area of Science:

    • Robotics
    • Control Theory
    • Artificial Intelligence

    Background:

    • Cooperative formation control for multiagent systems is challenging due to unknown velocities and system uncertainties.
    • Limited reference information further complicates achieving precise formation control and collision avoidance.

    Purpose of the Study:

    • To develop a collision-free cooperative formation control strategy for second-order multiagent systems.
    • To address challenges posed by unknown velocities, dynamics uncertainties, and limited reference information.

    Main Methods:

    • Proposed an observer-based sliding mode control law.
    • Introduced finite-time neural-based observers to estimate agent velocity and system uncertainty.
    • Utilized a sliding mode differentiator for approximating unknown derivatives of the formation reference.
    • Incorporated artificial potential fields and time-varying topology for collision avoidance.

    Main Results:

    • Ensured convergence of the system's tracking error.
    • Guaranteed boundedness of the relative distance between agents, preventing collisions.
    • Demonstrated effectiveness through numerical simulations on a multiple omnidirectional robot system.

    Conclusions:

    • The proposed limited-information-based control scheme effectively achieves collision-free cooperative formation control.
    • The integration of neural observers and sliding mode control enhances system robustness and performance.
    • The method provides a viable solution for complex multiagent coordination tasks.