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This paper discusses a learning algorithm for morphing aircraft, which targets the weaknesses of data-driven algorithms, mainly poor generalization ability and limited interpretability. This algorithm handles these constraints by integrating physical information with data-driven techniques. We propose a physics-informed SINDY (PI-SINDY) method framework, which improves the standard SINDY algorithm, to tackle strongly time-varying nonlinear flight systems. This method incorporates the physical information described by the aircraft’s differential kinematic equations into the SINDY algorithm, and also can handle the effects of measurement noise, making it more robust and practical. The proposed method displays higher robustness and generalization ability in comparison with the original SINDY algorithm and the WSINDy method, as confirmed by simulation results.

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System Identification Method Based on Interpretable Machine Learning for Unknown Morphing Aircraft

  • 会议论文

  • 发表日期：

    2025年

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  • Rui Cao,
  • Huitao Lv,
  • Liang Xu,
  • Yang Yang

摘要

This paper discusses a learning algorithm for morphing aircraft, which targets the weaknesses of data-driven algorithms, mainly poor generalization ability and limited interpretability. This algorithm handles these constraints by integrating physical information with data-driven techniques. We propose a physics-informed SINDY (PI-SINDY) method framework, which improves the standard SINDY algorithm, to tackle strongly time-varying nonlinear flight systems. This method incorporates the physical information described by the aircraft’s differential kinematic equations into the SINDY algorithm, and also can handle the effects of measurement noise, making it more robust and practical. The proposed method displays higher robustness and generalization ability in comparison with the original SINDY algorithm and the WSINDy method, as confirmed by simulation results.