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.
Hybridized Spacecraft Attitude Control via Reinforcement Learning using Control Moment Gyroscope Arrays
2021
161 pages
Report
Keine Angabe
Englisch
Computers, Control & Information Theory , Unmanned Spacecraft , Air force , Angular momentum , Artificial intelligence , Artificial intelligence software , Attitude control systems , Bayesian networks , Computational science , Computer languages , Computers , Control systems , Control systems engineering , Data science , Information science , Machine learning , Neural networks , Spacecraft , Supervised machine learning
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