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Learning Tracking Control Over Unknown Fading Channels Without System Information.

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    A new learning control method addresses unknown systems with fading sensor channels. This data-driven approach enables accurate tracking despite communication uncertainties, requiring no prior system knowledge.

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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Machine Learning

    Background:

    • Unknown systems and fading sensor channels pose significant challenges in control applications.
    • Traditional control methods often require detailed system models and direct output feedback, which are unavailable here.

    Purpose of the Study:

    • To develop a data-driven learning control scheme for unknown systems with unknown fading sensor channels.
    • To enable robust tracking performance in the presence of multiplicative and additive fading.

    Main Methods:

    • Proposed an error transmission mode where the reference is sent to the plant for local error calculation.
    • Developed an iterative gradient estimation method using only faded tracking error data.
    • Utilized a random difference technique for gradient estimation along the iteration axis.

    Main Results:

    • The proposed scheme effectively estimates the gradient for control signal updates without system or channel information.
    • Demonstrated successful tracking of desired references in simulated random fading communication environments.
    • The error transmission mode circumvents the need for direct output transmission through fading channels.

    Conclusions:

    • The novel data-driven learning control scheme is effective for unknown systems with fading channels.
    • The iterative gradient estimation and error transmission mode offer a robust solution for uncertain communication environments.
    • This approach eliminates the requirement for explicit system or channel identification.