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    This study introduces an intelligent data-driven predictive control strategy combining predictive control and local weighted projection regression. This novel approach offers improved performance for nonlinear processes and requires less prior knowledge than existing methods.

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    Area of Science:

    • Control Engineering
    • Machine Learning
    • Process Systems Engineering

    Background:

    • Traditional predictive control often requires extensive process knowledge and parameter tuning.
    • Existing data-driven methods, like dynamic Partial Least Squares (PLS), can be complex and less adaptable to nonlinear systems.

    Purpose of the Study:

    • To propose an intelligent data-driven predictive control strategy with enhanced performance for nonlinear processes.
    • To develop a controller that requires less prior knowledge and has fewer hard-to-determine parameters.
    • To enable online parameter updating for improved model validity and control intelligence.

    Main Methods:

    • Combining predictive control principles with local weighted projection regression (LWPR).
    • Implementing an online data-based parameter updating mechanism for the predictive model.
    • Utilizing LWPR for its ability to handle nonlinearities and reduce prior knowledge requirements.

    Main Results:

    • The proposed controller demonstrates superior performance in controlling nonlinear processes compared to dynamic PLS-based Model Predictive Control (MPC).
    • Achieved higher prediction precision and better adaptability to nonlinear dynamics.
    • Validated through simulations on a numerical example and a continuous stirred tank heater system.

    Conclusions:

    • The proposed intelligent data-driven predictive control strategy offers a robust and adaptive solution for nonlinear process control.
    • The integration of LWPR and online updating enhances control precision and reduces reliance on detailed process models.
    • This approach is well-suited for practical industrial applications demanding efficient nonlinear process management.