Aircraft Performance Prediction Model Based on Deep Learning

Aiming at the problems of traditional physical model methods in aircraft endurance prediction, an end-to-end prediction model based on depth deterministic policy gradient (DDPG) is proposed. The model realizes continuous mapping from flight parameters to range index through Actor-Critic dual network architecture, and combines experience playback mechanism and soft update strategy of target network to effectively suppress training oscillation and improve convergence stability. UAV Delivery Aircraft Versus hybrid dataset was used to verify model performance in test samples. The results show that the MAE of the model is 9.2 km, which is 42.1% lower than that of DQN; the prediction accuracy of the model is the best (MAE 7.3 km) in cruise phase, which is due to the dynamic compensation of time series difference error to wind speed disturbance; in environmental disturbance test, the error increment (50.0%) is significantly lower than that of DQN (78.0%) at low temperature (-5 ° C), which highlights its robustness to battery voltage sag. The model provides real-time and reliable decision support for aircraft endurance management in high-dynamic airspace.