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In this paper, a dynamic-programming approach to the coupled translational and rotational control of thruster-driven spacecraft is studied. To reduce the complexity of the problem, dynamic-programming-based optimal policies are calculated using decoupled position and attitude dynamics with generalized forces and torques as controls. A quadratic-programming-based control allocation is then used to map the controls to actuator commands. To control the spacecraft in the event of thruster failure, both the dynamic programming policies and control allocation are reconfigured to cope with the losses in controls. The control allocation parameters are adjusted dynamically to ensure the satellite always approaches the target from the side with two operative thrusters to achieve a stable control. The effectiveness of the proposed dynamic programming control is compared with a Lyapunov-stable control method, which shows that the proposed method is more fuel-efficient in tracking the same path.

The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for realworld robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming process that has not been able to scale to larger problems. In this work, we develop a policy optimization method by leveraging fast predictive sampling methods to process batches of trajectories in every forward pass, and compute gradient updates over policy parameters by automatic differentiation of Monte Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach in training policies on a set of reference-tracking control experiments with a heavy-duty machine. Benchmark results show a significant speedup over exact methods and showcase the scalability of our method to larger policy networks, longer horizons, and up to thousands of trajectories with a sublinear drop in speed.