Hybridized Spacecraft Attitude Control via Reinforcement Learning using Control Moment Gyroscope Arrays
Date of Award
Master of Science
Department of Aeronautics and Astronautics
Joshua A. Hess, PhD
Machine learning techniques in the form of reinforcement learning (RL) can solve complex nonlinear problems found within spacecraft attitude determination and control systems (ADCS). Three CMG arrays were implemented in two simulated spacecraft environments using a reinforcement learning controller. The performance of the controllers were evaluated using target profiles from traditional control law implementations, singularity measure, and variable initial state values. The current research demonstrates that while RL techniques can be implemented, further exploration is needed to investigate the operational efficacy of an approach for producing comparable performance attributes with respect to traditional control laws.
DTIC Accession Number
Agu, Cecily C., "Hybridized Spacecraft Attitude Control via Reinforcement Learning using Control Moment Gyroscope Arrays" (2021). Theses and Dissertations. 4983.