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    Area of Science:

    • Robotics
    • Control Systems
    • Neural Networks

    Background:

    • Parallel robots require robust real-time tracking control in complex environments with disturbances.
    • Conventional Zeroing Neural-Dynamics (ZND) models offer nonlinearity handling but struggle with unified disturbance rejection and finite-time convergence.

    Purpose of the Study:

    • To propose a novel Super-Twisting Zeroing Neural-Dynamics (ST-ZND) model.
    • To address the limitations of conventional ZNDs in handling external disturbances and achieving finite-time convergence for parallel robot control.

    Main Methods:

    • Developed a novel ST-ZND model by integrating the Super-Twisting (ST) algorithm with ZND principles.
    • Conducted rigorous theoretical analyses for global stability, finite-time convergence, and robustness against external disturbances.
    • Validated the model through simulations and comparative studies on parallel robot tracking control tasks.

    Main Results:

    • The proposed ST-ZND model demonstrates global stability and finite-time convergence.
    • The model exhibits superior robustness against external disturbances compared to conventional methods.
    • Effectiveness and superiority were confirmed through illustrative examples and convergence tests.

    Conclusions:

    • The ST-ZND model provides a unified and effective framework for real-time tracking control of parallel robots.
    • This novel approach significantly enhances robustness and convergence properties in the presence of external disturbances.
    • The findings highlight the potential of ST-ZND for advanced robotic control applications.