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    This study introduces a privacy-preserved rolling optimization strategy (PP-ROS) for switched systems. It protects data accuracy by adding noise to prediction and control horizons, ensuring private control inputs and system outputs.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Applied Mathematics

    Background:

    • Differential privacy methods often add noise to transmitted data, potentially compromising control system accuracy.
    • Data accuracy is critical for the performance of control systems.

    Purpose of the Study:

    • To propose a novel privacy-preserved rolling optimization strategy (PP-ROS) for switched systems.
    • To address the challenge of maintaining data accuracy while ensuring privacy in control systems.

    Main Methods:

    • Developed PP-ROS by adding Laplace noise to prediction and control horizons, not transmitted data.
    • Presented privacy definitions for horizons and designed a private model predictive control (P-MPC) controller.
    • Provided methods to prove and calculate privacy levels for control input and system output.

    Main Results:

    • The PP-ROS and P-MPC ensure privacy of controller parameters, control input, and system output.
    • Adding noise to horizons effectively preserves data accuracy compared to traditional methods.
    • Simulation examples demonstrate the availability and benefits of the proposed strategy.

    Conclusions:

    • The PP-ROS and P-MPC offer a viable solution for privacy-preserving control in switched systems.
    • The proposed method effectively balances data privacy with system performance and accuracy.