Intelligent flight control of combat aircraft based on autoencoder

The intelligent flight control of the aircraft is the key process in the air combat maneuver process. The traditional flight control method has many steps, long time and low precision, which have great drawbacks in the air combat process. In this paper, based on the background of deep learning, a flight control model based on autoencoder is proposed. Using the characteristics of autoencoder dimension reduction and feature extraction, the low-dimensional attitude parameters of high-dimensional aircraft can be extracted from high-dimensional flight attitude parameters. The eigenvalues are then automatically obtained through the neural network to change the attitude control of the aircraft. In this paper, the basic framework and training methods of the model are designed, and the influence of various parameters of the autoencoder network on the performance of the model is deeply studied. The experimental results show that the proposed model has better prediction accuracy and convergence performance than the traditional BP neural network, and achieves the purpose of intelligently and quickly obtaining flight attitude control to intelligently control aircraft flight.