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    This study shows robust iterative learning control (ILC) can handle nonrepetitive model uncertainties. A contraction mapping approach with an H∞-norm condition ensures convergence for improved control system performance.

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    Area of Science:

    • Control Engineering
    • Systems Science
    • Applied Mathematics

    Background:

    • Iterative learning control (ILC) refines control performance using past data.
    • Traditional ILC requires strict system repetitiveness, limiting its application.
    • Nonrepetitive, iteration-dependent uncertainties pose challenges for ILC robustness.

    Purpose of the Study:

    • To investigate robust ILC solutions for nonrepetitive model uncertainties.
    • To develop convergence conditions for ILC using a contraction mapping approach.
    • To determine if ILC can accommodate iteration-dependent plant model variations.

    Main Methods:

    • Utilized a contraction mapping (CM)-based approach for ILC analysis.
    • Proposed an H∞-norm condition to guarantee robust ILC convergence.
    • Derived learning gain matrices from the H∞-norm condition.

    Main Results:

    • Demonstrated that robust ILC can effectively address nonrepetitive model uncertainties.
    • Confirmed the applicability of the CM-based approach to robust ILC.
    • Simulation results validated the proposed H∞-based analysis and convergence conditions.

    Conclusions:

    • The CM-based approach enables robust ILC with iteration-dependent uncertainties.
    • The H∞-norm condition provides a method to ensure robust ILC convergence.
    • This research expands ILC applicability to systems with varying plant models.