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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(1): 5-17
M. Fu, “A tutorial on quantized feedback control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 5–17, Jan. 2024. doi: 10.1109/JAS.2023.123972
Citation: M. Fu, “A tutorial on quantized feedback control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 5–17, Jan. 2024. doi: 10.1109/JAS.2023.123972
shu

A Tutorial on Quantized Feedback Control

doi: 10.1109/JAS.2023.123972
Funds:  This work was partially supported by National Natural Science Foundation of China (62350710214, U23A20325) and Shenzhen Key Laboratory of Control Theory and Intelligent Systems (ZDSYS20220330161800001)
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    Abstract
  • In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation, achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.

     

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    • 1 A system is said to be quadratically stable if its stability can be asserted by using a quadratic Lyapunov function.
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    Highlights

    • This tutorial paper delves into quantized feedback control, tackling the challenges associated with transmitting feedback information, including measurements and control signals, over digital networks. Additionally, it explores strategies to mitigate the impact of quantization errors
    • Examined topics include minimal information for effective control, quantizer design, strategies for quantized control and estimation, achieving consensus control with quantized data, and high-precision tracking using quantized measurements
    • Provided insights into techniques addressing diverse quantized feedback problems, such as connecting logarithmic quantization to robust control under sector-bounded uncertainties, practical implementation of dynamic scaling for quantization, analysis of the absence of a separation principle for quantized LQG control, exploration of the tradeoff between average consensus precision and convergence rate, and strategies for mitigating quantization error in periodic signal tracking control

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