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Peer-reviewedCC BY 4.0Mathematics

Input Estimation for Repetitive-Motion Plants via ILC Subject to Quantization Errors and Packet Dropouts

  • Haixu Niu1
  • Zhichao Ding2
  • Lixun Huang3
  1. 1College of Physical Education, Henan University of Technology, Zhengzhou 450000, China
  2. 2School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China
  3. 3School of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou 450000, China
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Abstract

Quantization errors and packet dropouts in noisy communication channels degrade the convergence performance of repetitive-motion plants under iterative learning control (ILC). Different from existing works focusing on the redesign of ILC schemes, this paper regards the ILC-generated input as the iteration-domain state of the learning process, and proposes an estimation approach to reconstruct the actual input. Provided that the basic ILC scheme achieves convergence under ideal communication conditions free of quantization and packet dropouts, the proposed estimator mitigates the plant-side input deviation via input reconstruction in the presence of such disturbances in input–output channels. Simulation results illustrate the potential of the reconstructed input to improve the tracking performance of the controlled plant under such channel disturbances.

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Cite

BibTeXRIS
BibTeX
@article{niu2026input,
  title = {{Input Estimation for Repetitive-Motion Plants via ILC Subject to Quantization Errors and Packet Dropouts}},
  author = {Haixu Niu and Zhichao Ding and Lixun Huang},
  journal = {Mathematics},
  year = {2026},
  month = nov,
  doi = {10.3390/math14213981},
  url = {https://www.mdpi.com/2227-7390/14/21/3981},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Input Estimation for Repetitive-Motion Plants via ILC Subject to Quantization Errors and Packet Dropouts
AU  - Haixu Niu
AU  - Zhichao Ding
AU  - Lixun Huang
PY  - 2026
DA  - 2026/11/01
JO  - Mathematics
DO  - 10.3390/math14213981
UR  - https://www.mdpi.com/2227-7390/14/21/3981
AB  - Quantization errors and packet dropouts in noisy communication channels degrade the convergence performance of repetitive-motion plants under iterative learning control (ILC). Different from existing works focusing on the redesign of ILC schemes, this paper regards the ILC-generated input as the iteration-domain state of the learning process, and proposes an estimation approach to reconstruct the actual input. Provided that the basic ILC scheme achieves convergence under ideal communication conditions free of quantization and packet dropouts, the proposed estimator mitigates the plant-side input deviation via input reconstruction in the presence of such disturbances in input–output channels. Simulation results illustrate the potential of the reconstructed input to improve the tracking performance of the controlled plant under such channel disturbances.
SN  - 2227-7390
ER  -