  One major challenge for autonomous attitude takeover control for on-orbit
servicing of spacecraft is that an accurate dynamic motion model of the
combined vehicles is highly nonlinear, complex and often costly to identify
online, which makes traditional model-based control impractical for this task.
To address this issue, a recursive online sparse Gaussian Process (GP)-based
learning strategy for attitude takeover control of noncooperative targets with
maneuverability is proposed, where the unknown dynamics are online compensated
based on the learnt GP model in a semi-feedforward manner. The method enables
the continuous use of on-orbit data to successively improve the learnt model
during online operation and has reduced computational load compared to standard
GP regression. Next to the GP-based feedforward, a feedback controller is
proposed that varies its gains based on the predicted model confidence,
ensuring robustness of the overall scheme. Moreover, rigorous theoretical
proofs of Lyapunov stability and boundedness guarantees of the proposed
method-driven closed-loop system are provided in the probabilistic sense. A
simulation study based on a high-fidelity simulator is used to show the
effectiveness of the proposed strategy and demonstrate its high performance.
