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<p>The issue of iterative learning control (ILC) for distributed parameter systems with sensor/actuator networks is explored. Unlike the traditional setting of ILC where the desired trajectories are identical, here the desired trajectories are iteratively varying and characterized by a high-order internal mode (HOIM). To address this challenge, the D-type ILC algorithm based on HOIM is devised in this paper. This algorithm enables the systems to accurately track the desired trajectories that vary with each iteration. Using the principle of compressive mapping, the convergence conditions of the output error of the systems are given. In conclusion, numerical simulations are performed to verify the effectiveness of the proposed algorithm.</p>

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Iterative Learning Control for Distributed Parameter Systems with Sensor/Actuator Networks Based on High-Order Internal Mode

  • 期刊论文

  • 发表日期：

    2025年6月18日

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  • Jianxiang Zhang,
  • Manqi Mao

摘要

The issue of iterative learning control (ILC) for distributed parameter systems with sensor/actuator networks is explored. Unlike the traditional setting of ILC where the desired trajectories are identical, here the desired trajectories are iteratively varying and characterized by a high-order internal mode (HOIM). To address this challenge, the D-type ILC algorithm based on HOIM is devised in this paper. This algorithm enables the systems to accurately track the desired trajectories that vary with each iteration. Using the principle of compressive mapping, the convergence conditions of the output error of the systems are given. In conclusion, numerical simulations are performed to verify the effectiveness of the proposed algorithm.