Survey of machine learning techniques in spacecraft control design
Abstract In this paper, a survey on the machine learning techniques in spacecraft control design is given. Among the applications of machine learning on the subject are the design of optimal interplanetary trajectories, the synthesis of controllers to stabilize orbital or angular motion, formation control, the design of control laws for landing on the surface of a celestial body. All the works are classified into two almost equal groups the supervised learning (stochastic and deterministic methods) and the reinforcement learning (direct and value based approaches) Stochastic supervised learning methods are based on stochastic optimization procedures, random initialization of neural networks weights, and stochastic nature of the obtained results. Deterministic methods are based on the Lyapunov theory; the network training is a deterministic process. The division of reinforcement learning methods into direct and value based approaches is similar to the separation into direct and indirect methods in the optimal control theory. We discuss the main ideas, advantages, and drawbacks of the techniques and give some recommendations for future investigations. We also highlight interesting ideas and approaches in the application of machine learning methods that can be used in a broad variety of astrodynamical problems.