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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Adaptive Formation Control of Nonlinear High-Order Fully Actuated Multiagent Systems With Full-State Constraints and

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    Summary
    This summary is machine-generated.

    This study presents an adaptive formation control for high-order fully actuated multiagent systems with unknown dynamics and constraints. The novel framework ensures safety and tracking performance using nonlinear mapping and neural networks.

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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Multiagent systems (MASs) often face challenges with unknown nonlinear dynamics and state constraints.
    • Existing formation control methods may require strict feasibility conditions, limiting their applicability.

    Purpose of the Study:

    • To develop an adaptive formation tracking control for high-order fully actuated (HOFA) MASs.
    • To address unknown nonlinear dynamics and full-state constraints in MASs.
    • To ensure system safety and achieve desired formation performance.

    Main Methods:

    • A novel hierarchical formation control framework is proposed.
    • A nonlinear mapping function (NMF) transforms the constrained system into an unconstrained model.
    • Distributed observers and adaptive controllers are designed using fully actuated theory and NN approximators.
    • Lyapunov stability theory is employed to guarantee system stability and constraint satisfaction.

    Main Results:

    • The NMF effectively removes feasibility conditions required by traditional methods.
    • NN approximators successfully learn unknown nonlinear dynamics.
    • The proposed controller ensures states remain within constraints while achieving formation tracking.
    • The algorithm's effectiveness is demonstrated on multiple robotic arm systems.

    Conclusions:

    • The developed hierarchical adaptive formation control framework is effective for HOFA MASs.
    • The approach successfully handles unknown dynamics and state constraints.
    • This method offers a robust solution for complex multiagent coordination tasks.