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    This study introduces a data-driven tube-based robust predictive control (DTRPC) strategy for wastewater treatment processes. It ensures stable dissolved oxygen concentration (DOC) tracking despite nonlinearities and disturbances.

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    Area of Science:

    • Environmental Engineering
    • Control Systems Engineering
    • Wastewater Treatment Technology

    Background:

    • Wastewater treatment processes (WWTP) exhibit unknown nonlinearity and external disturbances, complicating dissolved oxygen concentration (DOC) control.
    • Maintaining DOC within operational constraints is crucial for effective wastewater treatment but challenging due to system uncertainties.
    • Existing control strategies often struggle with the inherent complexity and unpredictable nature of WWTP dynamics.

    Purpose of the Study:

    • To propose a novel data-driven tube-based robust predictive control (DTRPC) strategy for stable DOC tracking in WWTP.
    • To address system constraints and external disturbances inherent in wastewater treatment processes.
    • To overcome the challenges of modeling nonlinear and dynamically complex WWTPs.

    Main Methods:

    • Designed a tube-based robust predictive control (TRPC) strategy with nominal and auxiliary feedback controllers.
    • Employed two fuzzy neural network identifiers for accurate predictive modeling of nonlinear WWTP dynamics.
    • Utilized the generalized multiplier method and gradient descent algorithm for control law optimization.

    Main Results:

    • The DTRPC strategy successfully achieved stable tracking control of DOC within operational constraints.
    • The proposed method effectively suppressed external disturbances and restored nominal system performance.
    • Simulations on a benchmark model demonstrated the feasibility, stability, and effectiveness of the DTRPC strategy.

    Conclusions:

    • The developed DTRPC strategy offers a robust and data-driven solution for controlling DOC in complex WWTPs.
    • The combination of fuzzy neural networks and tube-based predictive control enhances system performance and constraint satisfaction.
    • This approach provides a reliable method for managing WWTPs with unknown nonlinearities and disturbances.