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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Robot manipulators often face challenges with dead-zone input, which can degrade control performance.
    • Existing adaptive neural network (NN) control methods may directly approximate system nonlinearities, potentially limiting effectiveness.
    • Ensuring stability and precise tracking in robotic systems with input nonlinearities remains a key research area.

    Purpose of the Study:

    • To develop a novel adaptive neural network (NN) tracking control strategy for robot manipulators with dead-zone input.
    • To enhance system stability and achieve high-performance tracking despite unknown nonlinear dynamics.
    • To introduce a new method employing NNs to identify virtual control signals rather than direct nonlinearity approximation.

    Main Methods:

    • Adaptive backstepping control technique combined with Lyapunov stability theory.
    • Utilization of neural networks (NNs) to identify unknown nonlinear items within virtual control signals.
    • Design of a sequence of virtual control signals and a real controller for the robot manipulator.

    Main Results:

    • The proposed adaptive NN controller guarantees semiglobally uniformly ultimately bounded (SGUUB) states for the system.
    • The controller ensures that the robot manipulator's output closely tracks the desired reference trajectory.
    • The effectiveness of the adaptive control strategy was validated through application to the Puma 560 robot manipulator.

    Conclusions:

    • The developed adaptive NN tracking control method effectively addresses dead-zone issues in robot manipulators.
    • The strategy provides robust stability guarantees and precise trajectory tracking performance.
    • This approach offers a promising alternative for controlling robotic systems with complex input nonlinearities.