Robust Adaptive Learning Control for Spacecraft Autonomous Proximity Maneuver

Abstract

The robust adaptive learning control for spacecraft autonomous proximity is investigated in this paper. The relative motion dynamics is firstly proposed and transformed into the state space representation. An adaptive learning controller that allows performing autonomous proximity maneuvers in the presence of uncertain orbital parameters is then developed. The unknown disturbances are considered, and a modified adaptive learning controller is therefore designed in the presence of uncertain parameters and unknown disturbances. The asymptotic stability of the proposed controllers for the closed-loop systems is proven based on the Lyapunov theory. Different numerical results are finally provided and compared to illustrate the performance of the proposed adaptive learning controllers.