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Predictive iterative learning control based on averaging technology for networked systems with fading channels and

Zhenxuan Li1, Zhiyang Zhang1, Chenkun Yin2

  • 1Beijing Institute of Petrochemical Technology, Beijing, China.

ISA Transactions
|July 27, 2025
PubMed
Summary

This study introduces a general averaging-based predictive iterative learning control (GA-PILC) for networked systems facing data loss and fading channels. The method ensures control accuracy despite communication imperfections.

Keywords:
Data lossFading channelsNetworked systemsPredictive iterative learning control

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Area of Science:

  • Control Engineering
  • Networked Systems
  • Signal Processing

Background:

  • Networked systems often suffer from data loss and fading channels, degrading control performance.
  • Iterative learning control (ILC) is effective for repetitive tasks but sensitive to communication issues.

Purpose of the Study:

  • To develop a robust predictive iterative learning control (PILC) method for networked systems with data loss and fading channels.
  • To mitigate the adverse effects of channel impairments on control accuracy.

Main Methods:

  • A general averaging (GA) technique is integrated into the prediction model and controller.
  • Control inputs are updated selectively upon successful data transmission.
  • A composite energy function method is employed in a stochastic framework to analyze convergence.

Main Results:

  • The proposed GA-based PILC method effectively eliminates the adverse effects of data loss and fading channels.
  • Convergence of the mean tracking error is theoretically proven.
  • The method's effectiveness is demonstrated through simulations.

Conclusions:

  • The GA-based PILC offers a robust solution for controlling networked systems with unreliable communication channels.
  • This approach enhances control system reliability and performance in challenging environments.