Synthesis of Data-Driven LightGBM Controller for Spacecraft Attitude Control

The attitude dynamics of spacecraft are highly nonlinear, which makes it challenging to design control algorithms that can handle these nonlinearities. This study presents a novel data-driven synthesis method for spacecraft attitude control using the LightGBM controller. The LightGBM controller is designed by using supervised machine learning methodologies. The training and testing datasets for the LightGBM controller are generated from the input-output data of a closed-loop system of spacecraft attitude dynamics under an exact feedback linearization-based controller. We show that under some realistic conditions, even though we can not guarantee asymptotic stability for the closed-loop system under the LightGBM controller, but we can have a kind of practical stability, i.e., we can have a smaller bounded ball by designing a LightGBM controller with a smaller bound of error. Furthermore, the simulation results show an additional interesting phenomenon that the LightGBM controller still produces good closed-loop performance even though there is uncertainty in satellite parameters and disturbance.