Transformer-Based Robust Feedback Guidance for Atmospheric Powered Landing
J. Carradori (Student TU Delft)
Marco Sagliano (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
E. Mooij (TU Delft - Astrodynamics & Space Missions)
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Abstract
Rocket reusability is a key factor in enabling quicker and more cost-effective access to space. However, landing on Earth poses significant challenges due to the dynamic and highly uncertain environment. A robust Guidance, Navigation, and Control system is essential to guide the vehicle to the landing site while meeting terminal constraints and minimizing fuel consumption. This research integrates Meta-Reinforcement Learning with Gated Transformer XL Neural Networks to enhance the robustness of the powered guidance with respect to atmospheric and aerodynamic uncertainties, navigation and control errors, and dispersed initial conditions. By employing a 6-Degrees-of-Freedom dynamics model and accurate vehicle and environmental simulations, the agent learns a higher fidelity guidance policy compared to existing literature, demonstrating successful and robust performance in Monte Carlo simulations. In this complex scenario, the innovative attention-based neural networks also outperform recurrent neural networks, widely used for Reinforcement Learning-based space guidance applications.