基于深度学习的时间角度控制制导律

A deep learning-based impact time and angle control guidance algorithm is proposed to solve the impact time and angle control problem of missile guidance under aerodynamic force. A predictor module is designed for estimating the exact impact time under optimal impact angle guidance law with realistic aerodynamic characteristics, and a feedforward loop for fusing the theoretical model and deep learning method is presented. An approximate time-to-go is predicted by the theoretical model and the prediction error is estimated by deep neural networks, so that the accuracy of prediction is improved. The impact time error could converge to zero after introducing the exact impact time into guidance law, then the Joint control of the impact time and the terminal impact angle is achieved. The Simulation results show that the designed guidance algorithm can realize the impact time and angle control more accurately compared with that based on a constant velocity assumption.