Asymmetric Deep Reinforcement Learning-Based Spacecraft Approaching Maneuver Under Unknown Disturbance

Spacecraft approaching maneuver control normally uses traditional control methods such as Proportional–Integral–Derivative (PID) or Model Predictive Control (MPC), which require meticulous system design and lack robustness against unknown disturbances. To address these limitations, we propose an end-to-end asymmetric Deep Reinforcement Learning-based (DRL) spacecraft approaching maneuver (ADSAM) algorithm, which significantly enhances the robutsness of the space-approaching maneuver under large-scale unknown disturbance and Partial Observation Markov Decision Processes (POMDPs). We present a numerical simulation environment with the linear Clohessy–Wiltshire (CW) model, incorporating the Runge–Kutta 4th order method (RK4) to ensure a more accurate and efficient state transition. Experimental results also demonstrate that the effectiveness of the proposed algorithm outperforms the-state-of-the-art methods.