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Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach
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Adaptive Iterative Learning Reliable Control of Nonrepetitive Systems With Multiple Iteration-Varying Parametric

Yong Chen, Deqing Huang, Xuefang Li

    IEEE Transactions on Cybernetics
    |February 13, 2026
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    Summary
    This summary is machine-generated.

    This study introduces an adaptive iterative learning control scheme for nonrepetitive systems, overcoming previous limitations. The new method effectively handles uncertainties, actuator faults, and state delays for improved practical applications.

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    Area of Science:

    • Control Engineering
    • Adaptive Control Systems
    • Nonlinear Systems

    Background:

    • Iterative learning control (ILC) is hindered by its repetitiveness requirement.
    • Practical applications of ILC are limited by nonrepetitive systems and uncertainties.
    • Existing control methods struggle with simultaneous actuator faults and state delays.

    Purpose of the Study:

    • To propose a novel adaptive iterative learning reliable control (ILRC) scheme.
    • To address nonrepetitive systems with iteration-varying parametric uncertainties.
    • To simultaneously consider actuator faults and state delays in the ILRC design.

    Main Methods:

    • Leveraging class- $k_{\infty }$ functions and neural networks to manage unmodeled dynamics.
    • Implementing control signal transformation to compensate for actuator inefficiencies.
    • Developing an innovative parametric estimation mechanism with hyperbolic tangent functions and auxiliary sequences for nonrepetitive uncertainties.

    Main Results:

    • Achieved zero-error convergence of system output.
    • Successfully compensated for actuator faults and state delays.
    • Demonstrated effective handling of iteration-varying parametric uncertainties.

    Conclusions:

    • The proposed ILRC scheme offers enhanced performance and practicality over existing methods.
    • The approach requires weak assumptions on system dynamics and minimal prior knowledge of uncertainties.
    • The controller exhibits strong learning capabilities, making it suitable for complex, nonrepetitive systems.