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Related Experiment Videos

Composite intelligent learning-based tracking control for discrete-time repetitive process.

Rongni Yang1, Jianqiang Hao1, Peng Shi2

  • 1School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.

ISA Transactions
|March 27, 2025
PubMed
Summary
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A new composite iterative learning control (ILC) algorithm enhances tracking performance for repetitive discrete-time systems. This two-phase approach, combining gain-adaptive and sliding mode control, improves convergence and precision.

Area of Science:

  • Control Systems Engineering
  • Robotics and Automation
  • Signal Processing

Background:

  • Repetitive discrete-time systems require precise tracking control for finite-duration tasks.
  • Existing iterative learning control (ILC) methods may face challenges in achieving both fast convergence and high precision.

Purpose of the Study:

  • To develop a novel composite iterative learning control (ILC) algorithm for discrete-time systems with repetitive tasks.
  • To enhance tracking performance by integrating two distinct control phases.

Main Methods:

  • A two-phase intelligent learning process: gain-adaptive iterative learning control (GAILC) and sliding mode iterative learning control (SMILC).
  • Phase switching is dynamically determined by the tracking error.
  • GAILC utilizes a prediction-based adaptive gain sequence for rapid error convergence.
Keywords:
Iterative learning control (ILC)Repetitive processSliding mode control (SMC)Tracking

Related Experiment Videos

  • SMILC employs a novel sliding surface and a fractional power term for high tracking precision.

    • Main Results:

    • The proposed composite ILC algorithm demonstrates improved tracking performance.
    • Comparative simulations, including a DC motor example, validate the effectiveness of the GAILC-SMILC strategy.
    • The two-phase approach achieves both fast convergence and high precision in tracking.

    Conclusions:

    • The developed composite ILC strategy offers a significant advantage for repetitive discrete-time systems.
    • The intelligent two-phase learning process effectively addresses tracking challenges.
    • The method shows promise for applications requiring precise repetitive motion control.