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August 19, 2026Transactions of the Institute of Measurement and Control

Event-triggered adaptive iterative learning control for two-dimensional Fornasini–Marchesini model systems

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Authors

QXQing‐Yuan XuBWBo-Xian WangYFYuan Fang

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Overview

Simulation study reveals tracking error convergence in two-dimensional discrete systems under event-triggered adaptive control, suggesting reduced communication and computational load.

Key Points

  • To develop an event-triggered adaptive iterative learning control scheme for two-dimensional linear discrete-time systems described by the Fornasini–Marchesini model to conserve communication resources.
  • Designed an event-triggering mechanism reliant exclusively on system state and tracking error to govern control updates.
  • Formulated an adaptive parameter update law across iterative cycles and integrated it into a two-dimensional control framework.
  • Analyzed system stability and error convergence using a composite energy function and validated performance with a numerical simulation.
  • Rigorous theoretical analysis via a composite energy function proved that tracking errors converge asymptotically across iterations.
  • Numerical simulation confirmed significant reductions in control input update frequency, mitigating controller computational burden and conserving communication bandwidth.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a85632803308d306e2d6300https://doi.org/10.1177/01423312261477126
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