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    Area of Science:

    • Control Engineering
    • Communication Systems
    • Signal Processing

    Background:

    • Networked control systems are increasingly common due to advancements in communication technologies.
    • Signal fading in communication channels presents a significant challenge to maintaining control performance.
    • Existing research has not fully addressed guaranteeing control performance under unknown fading conditions.

    Purpose of the Study:

    • To develop a learning strategy for improving tracking performance in networked control systems with unknown fading channels.
    • To address both output and input fading scenarios.
    • To provide a robust method for correcting biased signals transmitted through fading channels.

    Main Methods:

    • An iterative estimation mechanism is employed to gather statistical information for signal correction.
    • Learning control algorithms with decreasing step-sizes are designed for output and input fading.
    • Convergence analysis is performed in both mean-square and almost-sure senses.

    Main Results:

    • The proposed iterative estimation corrects biased signals effectively.
    • Learning control algorithms demonstrate convergence under mild conditions.
    • Simulations validate the effectiveness of the learning framework in improving tracking performance.

    Conclusions:

    • The developed learning strategy offers a viable solution for enhancing control performance in the presence of unknown fading channels.
    • The theoretical proofs and simulations confirm the robustness and effectiveness of the proposed methods.
    • This work contributes to the advancement of reliable networked control systems.