Oracle for Guidance with Deep Neural Networks in Reusable Launch Vehicle Landing

Oracles are of paramount importance for Deep Neural Networks training. In this paper, an oracle developed for landing reusable launch vehicles is created from a linearizing feedback control law that can perform a prescribed landing trajectory tracking. The oracle is then used to train a Deep Neural Network that can be used as a guidance system for landing maneuvers. Verification is performed by Monte-Carlo.