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    This study introduces a data-driven predictive control (PC) method for unknown nonlinear systems. It adaptively optimizes control gains using only input/output data, ensuring system stability.

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    Area of Science:

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

    Background:

    • Designing controllers for unknown nonlinear discrete-time systems presents significant challenges.
    • Model-based predictive control (PC) often requires accurate system models, which are unavailable for many real-world applications.

    Purpose of the Study:

    • To develop a novel, data-driven predictive control (PC) methodology for unknown nonlinear discrete-time systems.
    • To design a PC scheme that does not rely on a predefined system dynamic model.

    Main Methods:

    • The proposed method utilizes future ideal controllers and dynamic linearization (DL) to parameterize the control input increment vector.
    • A least squares method is employed to directly optimize the time-varying control gain vector.
    • System outputs are predicted using the parameterized PC law and the DL data model.

    Main Results:

    • The developed PC scheme is data-driven, adaptively optimizing the control gain vector solely from measured input/output data.
    • The monotonic convergence of the proposed PC scheme is theoretically guaranteed.
    • Effectiveness was demonstrated through simulations on both a complex nonlinear system and a linear time-invariant system.

    Conclusions:

    • The proposed data-driven predictive control methodology offers a robust approach for controlling unknown nonlinear discrete-time systems.
    • The technique eliminates the need for system identification, simplifying controller design and implementation.
    • Theoretical guarantees and practical examples confirm the efficacy and stability of the novel PC scheme.