基于物理信息神经网络的飞机姿态预测模型

doi:10.7527/S1000-6893.2025.31850

Abstract

Abstract:

In order to avoid the flight test safety accidents caused by exceeding the flight envelope during flight test， and reduce the accident risk， the subsequent attitude evolution process of the aircraft was predicted according to the current state information of the aircraft and the angle data of rudder deflection， so as to provide a basis for pilot decision-making support. Combining flight dynamics equation and Physical Information Neural Networks （PINNs）， a real-time aircraft attitude prediction model， Flight Dynamics-Physics Informed Neural Network （FD-PINN）， was constructed to solve the problem of insufficient prediction accuracy and poor generalization ability of the Neural Network-based （NN）aircraft attitude prediction model. Considering the randomness of atmospheric environmental parameters and the uncertainty of aircraft control inputs， flight simulation data were obtained by FlightGear to verify the model. The calculation results show that FD-PINN model has stronger generalization ability and higher prediction accuracy than NN model， and the mean square error of angle of attack prediction results is reduced by 68.5%.

Key words: attitude prediction, physical information neural networks, flight dynamics, flight test safety, flight test

CLC Number:

Yugang ZHANG, Zhe YANG, Senpeng HE, Wenqing YANG. Aircraft attitude prediction model based on physical information neural networks[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531850.

Figures/Tables 11

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超参数	数值
输入层神经元数量	17
隐藏层层数	2
隐藏层神经元数量	256，64
输出层神经元数量	4
L2正则化系数	0.1
优化器	Adam
权重初始化	Xavier初始化
激活函数	Tanh
迭代次数	1 000
批次大小	512
学习率	2×10^-5

预测评价指标		迎角/ （°）	侧滑角/ （°）	滚转角/ （°）	俯仰角/ （°）
MSE	NN	0.18	0.37	1.84	0.56
MSE	FD-PINN	0.09	0.26	1.77	0.26
MAE	NN	0.32	0.47	0.78	0.47
MAE	FD-PINN	0.19	0.38	0.73	0.27
R²	NN	0.977	0.826	0.977	0.990
R²	FD-PINN	0.988	0.876	0.978	0.995

预测评价指标		迎角/ （°）	侧滑角/ （°）	滚转角/ （°）	俯仰角/ （°）
MSE	NN	0.54	0.45	5.25	1.06
MSE	FD-PINN	0.17	0.24	2.53	0.66
MAE	NN	0.56	0.48	1.07	0.71
MAE	FD-PINN	0.19	0.37	0.87	0.42
R²	NN	0.968	0.631	0.970	0.977
R²	FD-PINN	0.989	0.803	0.985	0.985

Aircraft attitude prediction model based on physical information neural networks

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