Gaussian Process Regression Based Pose Tracking Control for Multiple Spacecraft Rendezvous

This paper proposes a data-based learning robust adaptive pose tracking controller for multiple spacecraft rendezvous with unknown dynamic uncertainties. Gaussian processes (GP) are used to accurately model and estimate unknown dynamic perturbations. In the case of approximate estimation error and external disturbances, a robust pose tracking controller is proposed. The stability of the closed-loop system is conformed by Lyapunov theory and the results of this paper is proved by a simulation example.