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    This study introduces adaptive formation control for nonlinear multiagent systems, ensuring angle rigidity and collision avoidance. The communication-free method handles uncertainties, achieving precise formation shapes.

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    Area of Science:

    • Robotics and Control Systems
    • Multiagent Systems Engineering
    • Nonlinear Dynamics

    Background:

    • Angle-constrained formation control is crucial for coordinated multiagent behavior, offering invariance under geometric transformations.
    • Existing methods often fail with complex nonlinear dynamics and unmatched uncertainties, limiting real-world applicability.
    • The need for robust, communication-free solutions for practical multiagent systems is evident.

    Purpose of the Study:

    • To develop an angle rigidity-based adaptive formation control framework for nonlinear multiagent systems.
    • To address challenges posed by mismatched uncertainties in multiagent systems.
    • To enable communication-free formation control while ensuring shape accuracy and collision avoidance.

    Main Methods:

    • Integration of prescribed performance control with recursive backstepping.
    • Development of an adaptive control law for angle rigidity preservation.
    • Utilization of local sensing for communication-free operation.

    Main Results:

    • The proposed framework effectively handles unmatched system uncertainties in nonlinear multiagent systems.
    • Angle rigidity is maintained throughout the formation process, ensuring shape integrity.
    • Asymptotic achievement of the desired triangulated formation shape is guaranteed without inter-agent collisions.
    • The control strategy operates effectively in a communication-free environment.

    Conclusions:

    • The developed angle rigidity-based adaptive formation control framework offers a robust solution for nonlinear multiagent systems.
    • The method's ability to handle uncertainties and operate without communication makes it suitable for practical applications.
    • Extensive simulations validate the effectiveness and reliability of the proposed control algorithms.