Gaussian-Process-Based Covariance Control for Autonomous On-Orbit Servicing

This paper presents a novel computational framework for the design of closed-loop guidance and control laws in highly uncertain environments, possibly characterized by unmodeled dynamics and external disturbances. The proposed methodology integrates the use of Gaussian process regression (GPR) to model uncertainties within a stochastic optimal control framework. GPR provides a probabilistic approach to disturbance estimation using simulated or observed data to characterize noise as a Gaussian process. Convexification techniques are used to transform the original nonconvex stochastic control problem into a sequence of deterministic convex problems that can be efficiently solved by state-of-the-art interior-point algorithms. As a study case, the docking maneuver between an active chaser spacecraft and a passive target spacecraft in the presence of differential drag, with uncertainty in the ballistic coefficient, is considered. The obtained numerical results suggest the effectiveness of the proposed approach in compensating for the disturbances and driving the system to the target final distribution.