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Model‐Free Adaptive Iterative Learning Control for Power Inverter With Measurement Noise

Zhenxuan Li et al.

Optimal Control Applications and Methods2026https://doi.org/10.1002/oca.70074article
ABDC B
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0.50

What the paper says

ABSTRACT The research and application of power inverters and related technologies represent the mainstream development of modern power electronics technology. For the unknown inverter system with measurement noise, a model‐free adaptive iterative learning control (MFAILC) scheme with a low‐pass filter is proposed. The purpose of this work is to achieve high tracking performance in the output voltage even when measurement noise exists. This method not only effectively overcomes the time‐varying parameter uncertainty in the inverter system by using the input/output data of the controlled plant, but also suppresses the measurement noise through the introduced filter. In order to verify the effectiveness of the convergence, tracking ability, and robustness of the proposed method, a simulation is conducted.

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https://doi.org/https://doi.org/10.1002/oca.70074

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@article{zhenxuan2026,
  title        = {{Model‐Free Adaptive Iterative Learning Control for Power Inverter With Measurement Noise}},
  author       = {Zhenxuan Li et al.},
  journal      = {Optimal Control Applications and Methods},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/oca.70074},
}

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Model‐Free Adaptive Iterative Learning Control for Power Inverter With Measurement Noise

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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