Flight Control Method Using Neural Network in Prediction for Suppressing Ship Airwake Impact in Carrier Landing
Yue Meng, Wei Wang, Hao Han
Abstract
Ship airwake is an important factor affecting the accurate carrier landing of carrier-based aircraft. To suppress the effects of airwake, this article presents a flight control method using neural network prediction. Different from most studies focusing on improving the adaptability of the flight control methods, the emphasis of the research is placed on modeling the effects of the ship airwake in the carrier landing environment pertinently and reflecting the effects in the control model. From a large amount of carrier landing flight data, the impact of airwake on the landing control can be extracted and summarized into models based on neural network algorithm. Then the airwake impact in the next period of time can be predicted. A flight control law is designed combining the predicting model of airwake impact with model predictive control. The simulation demonstrated that the presented method can reduce the trajectory deviation influenced by airwake and decrease the touchdown dispersions.