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Volume 6 Issue 5
Sep.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2019  >  6(5): 1196-1208
Ganggui Qu and Dong Shen, "Stochastic Iterative Learning Control With Faded Signals," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1196-1208, Sept. 2019. doi: 10.1109/JAS.2019.1911696
Citation: Ganggui Qu and Dong Shen, "Stochastic Iterative Learning Control With Faded Signals," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1196-1208, Sept. 2019. doi: 10.1109/JAS.2019.1911696
shu

Stochastic Iterative Learning Control With Faded Signals

doi: 10.1109/JAS.2019.1911696

  • College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Funds:

the National Natural Science Foundation of China 61673045

the Fundamental Research Funds for the Central Universities XK1802-4

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    Abstract
  • Stochastic iterative learning control (ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists. Illustrative examples are provided to verify the effectiveness of the proposed schemes.

     

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    Highlights

    • The paper provides a Kalman filtering-based approach to solve iterative learning control problem through random fading channels
    • Fading channels at both output and input sides are taken into account, modeled by a general multiplicative randomness form.
    • The learning gain matrix is recursively computed by minimizing the trace of input error covariance, which varies in both time and iteration domains.
    • The asymptotic convergence of the input sequence to the desired value is strictly proved in the mean-square sense.

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