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    This study presents a new adaptive neural network controller for the Furuta pendulum, an underactuated system. Real-time experiments show improved performance in tracking control while maintaining pendulum stability.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • The Furuta pendulum is a complex two-degree-of-freedom underactuated system.
    • Controlling underactuated systems presents significant challenges in robotics.

    Purpose of the Study:

    • Introduce a novel adaptive neural network-based control scheme for the Furuta pendulum.
    • Develop adaptation laws for input and output weights.
    • Guarantee reference signal tracking for the arm with pendulum stability.

    Main Methods:

    • Defined a novel output function dependent on position and velocity errors.
    • Rigorously analyzed internal and external dynamics.
    • Proved uniform ultimate boundedness of error trajectories.
    • Implemented real-time experimental validation.

    Main Results:

    • The proposed adaptive neural network controller ensures stable tracking.
    • Demonstrated superior performance compared to existing control methods.
    • Validated the controller's effectiveness through practical experiments.

    Conclusions:

    • The novel adaptive control scheme offers enhanced performance for the Furuta pendulum.
    • The controller guarantees stable operation and accurate tracking.
    • This approach advances control strategies for underactuated robotic systems.