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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Artificial Intelligence

    Background:

    • Actuator saturation poses significant challenges in nonlinear control systems.
    • Existing control methods may struggle with convergence time and robustness under saturation.

    Purpose of the Study:

    • To develop a fixed-time tracking control scheme for nonlinear systems with actuator saturation.
    • To ensure the convergence time is independent of initial conditions.
    • To enhance overall controller performance and system stability.

    Main Methods:

    • Approximation of the saturation function using a smooth nonlinear function.
    • Design of a controller within the backstepping framework.
    • Compensation for input saturation via an auxiliary system.
    • Application of fixed-time control theory and adaptive neural networks.
    • Reduction of controller dynamic order through a single updating law.

    Main Results:

    • Guaranteed convergence of output tracking error to a small neighborhood in fixed time.
    • Ensured boundedness of all signals within the closed-loop system.
    • Validation through numerical and practical examples, including a single-link manipulator.

    Conclusions:

    • The proposed fixed-time adaptive neural network control effectively addresses actuator saturation in nonlinear systems.
    • The control scheme offers improved performance with guaranteed convergence time and system stability.
    • The method demonstrates practical applicability in robotic systems.