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Data-based iterative learning control for nonlinear systems subject to iteration-dependent durations.

Yuxin Wu1, Deyuan Meng2, Jian Sun3

  • 1National Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, PR China.

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|December 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces data-based iterative learning control (ILC) for nonlinear systems with varying durations. A novel ILC updating law ensures perfect tracking by utilizing system data.

Keywords:
Data-based controlIteration-dependent durationIterative learning controlLocally Lipschitz nonlinearity

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Area of Science:

  • Control Engineering
  • Nonlinear System Dynamics
  • Machine Learning for Control

Background:

  • Iterative learning control (ILC) is crucial for repetitive tasks.
  • Locally Lipschitz nonlinear systems present challenges due to complex dynamics.
  • Iteration-dependent durations complicate traditional ILC approaches.

Purpose of the Study:

  • To develop a data-based ILC strategy for locally Lipschitz nonlinear systems with iteration-dependent durations.
  • To design an ILC updating law that effectively utilizes collected input-output data.
  • To establish conditions for achieving perfect tracking in such systems.

Main Methods:

  • A test framework for collecting iteration-specific input-output data.
  • An ILC updating law integrating modified outputs to counteract duration variations.
  • Analysis based on the persistent full-learning property.

Main Results:

  • A data-driven ILC updating law is proposed for nonlinear systems.
  • The method effectively compensates for iteration-dependent durations.
  • A necessary and sufficient condition for perfect tracking is derived based on output data.

Conclusions:

  • The developed data-based ILC is applicable to locally Lipschitz nonlinear systems with irregular dynamics.
  • The approach offers a robust solution for systems with time-varying operation lengths.
  • Perfect tracking is achievable under proposed data-dependent conditions.