Session
Technical Poster Session 3
Location
Utah State University, Logan, UT
Abstract
The focus of our initial application scenario centers around a low-thrust orbit transfer in Low-Earth Orbit (LEO). This specific use-case has been chosen due to its inherent challenges, including the requirements for robustness and real-time computation.
We propose an AI-based solution capable of autonomous and robust on-board G&C. The core of our approach leverages a Deep Neural Network (DNN) trained through Reinforcement Learning (RL) techniques. Our method aims at enhancing a traditional guidance approach by managing environmental perturbations, it processes the on-board navigation coordinates and provides the thrust to be imposed by the propulsion subsystem.
Our approach demonstrates effectiveness in performing maneuvers changing semi-major axis (SMA), eccentricity (ECC), and inclination (INC), operating continuously with a control horizon of several days. Robustness is tested by using physical model uncertainties, introducing disturbances in the mission coordinates, and injecting perturbations in subsystems.
Toward Autonomous Guidance and Control: A Robust AI-Based Solution for Low-Thrust Orbit Transfers
Utah State University, Logan, UT
The focus of our initial application scenario centers around a low-thrust orbit transfer in Low-Earth Orbit (LEO). This specific use-case has been chosen due to its inherent challenges, including the requirements for robustness and real-time computation.
We propose an AI-based solution capable of autonomous and robust on-board G&C. The core of our approach leverages a Deep Neural Network (DNN) trained through Reinforcement Learning (RL) techniques. Our method aims at enhancing a traditional guidance approach by managing environmental perturbations, it processes the on-board navigation coordinates and provides the thrust to be imposed by the propulsion subsystem.
Our approach demonstrates effectiveness in performing maneuvers changing semi-major axis (SMA), eccentricity (ECC), and inclination (INC), operating continuously with a control horizon of several days. Robustness is tested by using physical model uncertainties, introducing disturbances in the mission coordinates, and injecting perturbations in subsystems.
