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    Summary
    This summary is machine-generated.

    This study introduces Conic Input Mapping (CIM) for optimal iterative learning control (ILC) in uncertain systems. CIM uses process data to enhance control performance and speed up error reduction.

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    Area of Science:

    • Control Engineering
    • Systems Science
    • Applied Mathematics

    Background:

    • Modeling uncertainties are inherent in control systems, hindering convergence rates.
    • Process data is often abundant and can be leveraged to improve control performance.
    • Existing optimal iterative learning control (ILC) methods have limitations in handling uncertainties.

    Purpose of the Study:

    • To develop a novel Conic Input Mapping (CIM) methodology for optimal iterative learning control (ILC).
    • To incorporate online process data into optimal and robust optimal ILC designs for uncertain systems.
    • To improve control performance and accelerate convergence rates in iterative learning control.

    Main Methods:

    • A new Conic Input Mapping (CIM) methodology is proposed, utilizing convex cone theory.
    • Process data is mapped into control input design for the first time.
    • Optimal and robust optimal ILC methods are developed based on CIM for systems with bounded uncertainties.

    Main Results:

    • CIM-based optimal ILC and robust optimal ILC methods are developed and theoretically analyzed.
    • The proposed methods demonstrate improved control performance and faster convergence rates.
    • Numerical examples validate the effectiveness of the CIM methodology.

    Conclusions:

    • The Conic Input Mapping (CIM) methodology offers a novel approach to enhance optimal iterative learning control for uncertain systems.
    • Leveraging process data through CIM significantly improves control performance and convergence speed.
    • The developed CIM-based ILC methods provide a robust solution for complex control challenges.