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    This study introduces a new predefined time Zeroing Neural Network (ZNN) model using a polymorphous activation function (PAF). The novel approach reduces conservatism and enhances robot manipulator control performance.

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    Area of Science:

    • * Neural Networks and Control Systems
    • * Robotics and Automation
    • * Mathematical Optimization

    Background:

    • * Zeroing Neural Network (ZNN) models effectively address matrix inverse problems.
    • * Existing ZNN research often overlooks the conservatism associated with predefined convergence times.
    • * There is a need for ZNN models with improved convergence, robustness, and reduced time conservatism.

    Purpose of the Study:

    • * To develop a novel Non-conservatism Predefined Time ZNN (NPTZNN) model.
    • * To incorporate a polymorphous activation function (PAF) for enhanced performance.
    • * To reduce the conservatism of predefined time calculations in ZNN models.

    Main Methods:

    • * Construction of the NPTZNN model utilizing a polymorphous activation function (PAF).
    • * Application of the second method of Lyapunov for rigorous stability, convergence, and robustness analysis.
    • * Integration of the Beta function for calculating the predefined time, minimizing conservatism.

    Main Results:

    • * The NPTZNN model demonstrates reduced conservatism in its predefined time calculation.
    • * Theoretical analyses confirm the stability, convergence, and robustness of the NPTZNN model.
    • * Experimental validation verifies the model's effectiveness and correctness.

    Conclusions:

    • * The developed NPTZNN model offers a significant improvement over traditional ZNN approaches.
    • * The NPTZNN model enhances tracking speed in robot manipulator control applications.
    • * This research expands the practical applicability of ZNN models in complex control tasks.