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    This study introduces knowledge-data driven optimal control (KDDOC) to enhance nonlinear system performance. KDDOC adapts to changing demands, ensuring optimal operation through intelligent parameter setting and solution searching.

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    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • System Dynamics

    Background:

    • Traditional optimal control struggles with dynamically changing operational demands in nonlinear systems.
    • Achieving reliable optimal solutions for satisfying performance under variable conditions remains a challenge.

    Purpose of the Study:

    • To design a novel knowledge-data driven optimal control (KDDOC) framework for nonlinear systems.
    • To enhance the adaptability and efficiency of optimal control for systems with dynamic operational requirements.

    Main Methods:

    • An adaptive initialization strategy using historical data to dynamically preset KDDOC parameters.
    • A knowledge-guided global best selection mechanism for optimal solution searching under varying demands.
    • A knowledge-directed exploitation mechanism to accelerate the solving process and improve response speed.

    Main Results:

    • KDDOC demonstrates enhanced initial performance through adaptive parameter setting.
    • Dynamic optimal solutions are achieved, enabling adaptation to flexible changes in system operation.
    • Improved demand response speed ensures optimal system performance across different states.

    Conclusions:

    • The proposed KDDOC effectively addresses the limitations of traditional optimal control for nonlinear systems.
    • KDDOC provides a robust and adaptive solution for maintaining optimal operation under dynamic conditions.
    • Validation through simulation and practical processes confirms the effectiveness of the KDDOC approach.