Model Predictive Control with Gaussian-Process-Enhanced Dynamics for Spacecraft Attitude Takeover Maneuvers

This paper aims to deal with the attitude takeover control for combined spacecraft with unknown dynamics and attitude maneuverability of the target. Generally, the dynamics of combined spacecraft executing an on-orbit servicing task is highly nonlinear and costly to identify. To address this issue, a sparse variational Gaussian process (GP)-based model predictive control (MPC) strategy is proposed to achieve efficient attitude takeover maneuvers under multiple constraints and target attitude maneuverability. The GP regression performs a model completion behavior, where the unknown dynamics is compensated by the sparse variational GP with a low computational load. This enhances the control performance of the combined spacecraft during the takeover task execution. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy.