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A Probabilistically Quantized Learning Control Framework for Networked Linear Systems.

Dong Shen, Niu Huo, Samer S Saab

    IEEE Transactions on Neural Networks and Learning Systems
    |June 15, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a quantized learning control framework for networked systems, enhancing tracking performance while minimizing communication load. Proposed gain sequences effectively reduce errors to zero, unlike constant gain methods.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems

    Background:

    • Networked systems face challenges with communication burden and tracking performance.
    • Quantization and channel noise degrade control system accuracy.

    Purpose of the Study:

    • To develop a quantized learning control framework for linear networked systems.
    • To achieve high tracking performance with reduced communication load.
    • To analyze different gain sequences for error convergence.

    Main Methods:

    • Proposed an integrated framework with a probabilistic quantizer and a learning scheme.
    • Utilized a Bernoulli distribution for the probabilistic quantizer.
    • Investigated three learning control schemes: constant gain, decreasing gain, and optimal gain sequences.

    Main Results:

    • Constant gain control results in bounded mean-square input errors.
    • Proposed decreasing and optimal gain sequences drive mean-square input errors to zero.
    • Demonstrated varying convergence rates and robustness against uncertainties through simulations.

    Conclusions:

    • The proposed learning control schemes with specific gain sequences offer superior error convergence compared to constant gain.
    • The framework effectively balances tracking performance and communication efficiency in networked systems.