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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Control Theory

    Background:

    • Strict-feedback systems often face challenges with input saturation, limiting control performance.
    • Existing methods may struggle with differentiability or rely on complex compensation signals.

    Purpose of the Study:

    • To develop a novel adaptive control approach for strict-feedback systems under input saturation.
    • To enhance control flexibility and transient performance while ensuring stability.

    Main Methods:

    • Reformulating the performance function (PF) via adaptive modification of its exponential index.
    • Constructing a performance indicator function (PIF) using output-side information.
    • Designing a first-order auxiliary system for real-time adaptive index adjustment.

    Main Results:

    • The self-adjusting performance function (SAPF) dynamically balances input/output behaviors.
    • Performance boundaries are relaxed when constraint violations are imminent.
    • Transient performance is enhanced under saturation constraints.

    Conclusions:

    • The proposed SAPF-based control algorithm offers rigorous closed-loop stability guarantees.
    • Demonstrated effectiveness and superiority over advanced algorithms in vehicle lane-keeping simulations.