An Online Generation Method of Terminal-Area Trajectories for Wave-Rider Using Deep Neural Networks

Abstract

This paper presents a deep neural network-based online trajectory generation method for the aerodynamic characteristic description and terminal-area energy management of wave-rider aircrafts. First, the flight dynamics equations in the energy domain are linearized and discretized to generate numerous aircraft trajectory samples with sequential convex optimization (SCO) methods. Then, an optimization objective function is designed to promote the smoothness of the control variables and improve the trajectory similarity. Compared to the nonlinear programming (NLP), the proposed trajectory sample generation method is more suitable for the training of deep neural networks (DNNs). Finally, deep neural networks are formulated and trained for the control variables and state variables, using the generated obtained trajectory samples, so that the reference trajectories can be obtained online during the energy management process of the wave-rider’s terminal phase. Numerical simulations validate the high accuracy of the trajectories generated with the deep neural network. Meanwhile, this proposed method enables smaller storage usage, which is highly suitable for integration into on-board flight control systems.

Keywords

• sequential convex optimization
• deep neural networks
• online generation
• terminal-area
• wave-rider