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    An enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) method improves nonlinear discrete-time systems. This data-driven approach uses input-output data for effective control and monotonic convergence.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Dynamics
    • Data-Driven Control

    Background:

    • Iterative learning control (ILC) is crucial for repetitive tasks in discrete-time systems.
    • Nonlinear and nonaffine systems present significant control challenges.
    • Existing ILC methods often require precise system models.

    Purpose of the Study:

    • To propose an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) strategy.
    • To address control challenges in nonlinear and nonaffine discrete-time systems.
    • To develop a controller that relies solely on input-output data.

    Main Methods:

    • A dynamical linearization approach is employed to transform system dynamics.
    • An ILC law with nonlinear learning gain is designed.
    • A parameter updating law iteratively estimates unknown system partial derivatives.
    • Input signals are updated using terminal tracking errors and previous input signals.

    Main Results:

    • The proposed E-DDOTILC method effectively controls nonlinear and nonaffine discrete-time systems.
    • The approach demonstrates monotonic convergence of tracking errors.
    • Simulation results validate the controller's effectiveness.

    Conclusions:

    • The E-DDOTILC is a robust, data-driven control strategy for nonlinear discrete-time systems.
    • The method eliminates the need for explicit system models, relying only on I/O data.
    • The proposed approach offers a viable solution for complex control problems.