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<p>A non-iterative data-driven tuning method is proposed for linear fractional-order controllers. The formulation of the tuning process adopts a model reference control problem structure and is subsequently reformulated as a numerical optimization problem on the basis of a fictitious reference signal. This signal is computed using the controller under evaluation and a single set of input and output data from the controlled plant. We analyze the effect of noise corrupting the data to the data-based optimization problem. To mitigate noise corrupting the data used for controller tuning, the data is preprocessed using the <i>L</i><sub>2</sub> total variation regularization technique. The proposed approach is simple in that it requires neither mathematical modeling nor repeating closed-loop control tests. The incorporation of the <i>L</i><sub>2</sub> total variation denoising improves the noise tolerance of the proposed tuning technique. The validity of the proposed approach is demonstrated through numerical simulations.</p>

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One-shot Data-driven Tuning Approach for Fractional-order Controller Based on Fictitious Reference Signal and Total Variation Regularization

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Ansei Yonezawa,
  • Heisei Yonezawa,
  • Shuichi Yahagi,
  • Itsuro Kajiwara,
  • Shinya Kijimoto

摘要

A non-iterative data-driven tuning method is proposed for linear fractional-order controllers. The formulation of the tuning process adopts a model reference control problem structure and is subsequently reformulated as a numerical optimization problem on the basis of a fictitious reference signal. This signal is computed using the controller under evaluation and a single set of input and output data from the controlled plant. We analyze the effect of noise corrupting the data to the data-based optimization problem. To mitigate noise corrupting the data used for controller tuning, the data is preprocessed using the L2 total variation regularization technique. The proposed approach is simple in that it requires neither mathematical modeling nor repeating closed-loop control tests. The incorporation of the L2 total variation denoising improves the noise tolerance of the proposed tuning technique. The validity of the proposed approach is demonstrated through numerical simulations.