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Volume 10 Issue 5
May  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Z. H. Li, D. Shen, and X. H. Yu, “Enhancing iterative learning control with fractional power update law,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1137–1149, May 2023. doi: 10.1109/JAS.2023.123525
Citation: Z. H. Li, D. Shen, and X. H. Yu, “Enhancing iterative learning control with fractional power update law,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1137–1149, May 2023. doi: 10.1109/JAS.2023.123525

Enhancing Iterative Learning Control With Fractional Power Update Law

doi: 10.1109/JAS.2023.123525
Funds:  This work was supported by the National Natural Science Foundation of China (62173333) and Australian Research Council Discovery Program (DP200101199)
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  • The P-type update law has been the mainstream technique used in iterative learning control (ILC) systems, which resembles linear feedback control with asymptotical convergence. In recent years, finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback. In this paper, we show that such mechanism can equally ramp up the learning speed in ILC systems. We first propose a fractional power update rule for ILC of single-input-single-output linear systems. A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process. Using the nonlinear mapping approach, fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven. The limit cycles are shown to be tunable to determine the steady states. Numerical simulations are provided to verify the theoretical results.


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