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Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

Jason Sheng-Hong Tsai1, Yan-Yi Du, Pei-Hsiang Huang

  • 1Control System Laboratory, Department of Electrical Engineering, National Cheng-Kung University, Tainan 701, Taiwan, ROC. shtsai@mail.ncku.edu.tw

ISA Transactions
|February 22, 2011
PubMed
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This study introduces an iterative learning-based decentralized adaptive tracker to enhance control system performance. The new method improves dynamic tracking for large-scale systems, even with complex, non-analytic trajectories.

Area of Science:

  • Control Systems Engineering
  • Adaptive Control Theory
  • Digital Redesign Techniques

Background:

  • Large-scale control systems often face challenges with dynamic performance and inter-subsystem interference.
  • Sampled-data systems require precise digital control strategies to maintain desired outputs.
  • Decentralized adaptive control offers a robust approach for complex interconnected systems.

Purpose of the Study:

  • To propose a digital redesign methodology for an iterative learning-based decentralized adaptive tracker.
  • To improve the dynamic performance of sampled-data linear large-scale control systems.
  • To enable system outputs to follow arbitrary trajectories, even those not initially defined by an analytic reference model.

Main Methods:

  • Development of a model reference decentralized adaptive control scheme with a decoupled reference model.

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  • Application of optimal analog control and prediction-based digital redesign for the tracker.
  • Integration of iterative learning control (ILC) to train control inputs via continual learning.
  • Utilizing evolutionary programming to optimize the ILC learning gain.

    • Main Results:

    • The proposed tracker achieves robust closed-loop decoupled properties.
    • Good tracking performance is demonstrated at both transient and steady states.
    • The digital redesign methodology effectively enhances dynamic performance in sampled-data large-scale systems.
    • Iterative learning control significantly improves tracking accuracy at specified sampling instants.

    Conclusions:

    • The developed iterative learning-based decentralized adaptive tracker offers a robust and effective solution for sampled-data large-scale control systems.
    • The methodology successfully addresses challenges related to inter-subsystem interference and arbitrary trajectory following.
    • The combination of adaptive control, digital redesign, and iterative learning control provides enhanced dynamic performance and tracking accuracy.