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Neural-Learning-Based Control for a Constrained Robotic Manipulator With Flexible Joints.

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    This study introduces a novel neural network (NN) control for robotic manipulators with flexible joints (RMFJ) to address system uncertainties. The proposed controller enhances robustness and ensures stability, validated through simulations and experiments on a Baxter robot.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • The control technology for robotic manipulators with flexible joints (RMFJ) is immature, presenting significant challenges due to system uncertainties.
    • Designing effective controllers for these systems requires addressing inherent complexities and enhancing robustness.

    Purpose of the Study:

    • To propose a robust full-state feedback neural network (NN) control strategy for RMFJ.
    • To ensure output constraints and improve the operational security of RMFJ.
    • To enhance the stability and boundedness of system state variables in RMFJ.

    Main Methods:

    • Development of a full-state feedback neural network (NN) controller tailored for RMFJ dynamics.
    • Utilizing Lyapunov stability analysis to guarantee system stability and state variable boundedness.
    • Conducting simulation experiments and real-world control experiments on a Baxter robot for validation.

    Main Results:

    • The proposed NN controller effectively manages uncertainties in RMFJ systems.
    • Lyapunov stability analysis confirms the controller's ability to ensure system stability and state boundedness.
    • Experimental results on the Baxter robot demonstrate the feasibility and effectiveness of the NN control strategy.

    Conclusions:

    • The developed NN control method significantly enhances the robustness and security of RMFJ.
    • The controller provides a reliable solution for controlling flexible-joint robotic manipulators.
    • Comparative analysis validates the superiority of the proposed NN control over traditional methods.