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November 14, 2025Transactions of the Institute of Measurement and Control

Iterative learning control method for tracking quasi-sinusoidal signals with slowly varying random frequency perturbations

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Authors

ZHZhiying HeSXSiqi XiaoYZYong Zheng

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Overview

The novel iterative learning control algorithm improves tracking of quasi-sinusoidal signals in industrial applications, enhancing control precision and adaptability.

Key Points

  • Tracking performance improves significantly with adaptive learning algorithms, as shown by the reduced control error across iterations.
  • The iterative learning control methodology was employed on a time-invariant system, showcasing effective trajectory tracking capabilities.
  • Mathematical convergence analysis ensured the reliability of the proposed estimation methodology for frequency perturbation.
  • The algorithm's application in various industrial environments emphasizes its importance in addressing environmental changes.

Cite This Study

He et al. (2025) studied this question.

synapsesocial.com/papers/692519a2c0ce034ddc353d99https://doi.org/10.1177/01423312251384858
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