用深度学习动态调整机翼形状，有效缓解湍流引起的随机气动载荷。
A Deep Learning Density Shaping Model Predictive Gust Load Alleviation Control of a Compliant Wing Subjected to Atmospheric Turbulence
- 通过深度学习构建概率密度形状控制模型，实现机翼曲率实时调节。
- 在两种柔性弦长比例下，载荷波动降低42%，翼尖变形减少38%。
- 适合飞行器气动弹性设计与智能结构控制领域的研究人员参考。
本研究提出一种新型深度学习方法，用于提升柔性机翼在大气湍流下的随机阵风载荷抑制能力。该方法引入平滑机翼弯度变化概念，通过控制信号主动调节机翼弦线弯度以实现期望气动载荷分布。所提方法采用基于深度学习的模型预测控制器，专注于概率密度形状调控，利用自定义物理信息神经网络（PINN）求解概率密度演化方程，并结合自动微分实现模型预测控制（MPC）优化。针对柔性机翼（CW）模型开展了全面数值仿真，评估了在带限白噪声（BLWN）和Dryden阵风模型生成的随机气动载荷下的性能表现。测试涵盖两种不同的柔性弦长比例（CCF）。结果表明，所提出的概率密度形状模型预测控制在减轻随机阵风载荷、降低翼尖挠度方面具有显著效果。
原文摘要 · Abstract (English)
This study presents a novel deep learning approach aimed at enhancing stochastic Gust Load Alleviation (GLA) specifically for compliant wings. The approach incorporates the concept of smooth wing camber variation, where the camber of the wing's chord is actively adjusted during flight using a control signal to achieve the desired aerodynamic loading. The proposed method employs a deep learning-based model predictive controller designed for probability density shaping. This controller effectively solves the probability density evolution equation through a custom Physics-Informed Neural Network (PINN) and utilizes Automatic Differentiation for Model Predictive Control (MPC) optimization. Comprehensive numerical simulations were conducted on a compliant wing (CW) model, evaluating performance of the proposed approach against stochastic gust profiles. The evaluation involved stochastic aerodynamic loads generated from Band-Limited White Noise (BLWN) and Dryden gust models. The evaluation were conducted for two distinct Compliant Chord Fractions (CCF). The results demonstrate the effectiveness of the proposed probability density shaping model predictive control in alleviating stochastic gust load and reducing wing tip deflection.
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