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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Robotic manipulators often exhibit unknown deadzone effects, which can significantly degrade control performance and accuracy.
    • Uncertainty in manipulator dynamics further complicates precise motion control.

    Purpose of the Study:

    • To develop an adaptive neural network control strategy for robotic manipulators with unknown deadzone.
    • To enhance the practicality of the control scheme using a high-gain observer.
    • To estimate unknown manipulator dynamics and compensate for deadzone effects.

    Main Methods:

    • Introduced state-feedback control and designed a high-gain observer.
    • Utilized two radial basis function neural networks (RBFNNs): one for deadzone compensation and another for estimating unknown robot dynamics.
    • Verified the proposed control on a two-joint rigid manipulator.

    Main Results:

    • The adaptive neural network control effectively compensated for unknown deadzone effects.
    • The high-gain observer improved the practical applicability of the control system.
    • Numerical simulations and experimental results demonstrated the efficacy of the proposed method.

    Conclusions:

    • The proposed adaptive neural network control scheme is effective for robotic manipulators with unknown deadzone.
    • The integration of RBFNNs and a high-gain observer offers a robust solution for complex robotic control problems.