The Maintenance of Orbital States in a Floating Partial Space Elevator Using the Reinforcement Learning Method

A partial space elevator (PSE) is a multi-body tethered space system in which the main satellite, typically an ultra-large spacecraft or a space station in a higher orbit, is connected to a transport spacecraft in a lower orbit via a tether, maintaining orbital synchronization. One or more climbers can move along the tether driven by electric power, enabling cross-orbital payload transportation between the two spacecraft. The climbers’ motion significantly alters the main satellite’s orbital states, compromising its safe and stable operation. The dynamic coupling and nonlinearity of the PSE further exacerbate this challenge. This study aims to preliminarily address this issue by proposing a new mission planning strategy. This strategy utilizes reinforcement learning (RL) to select the waiting interval between two transfer missions, thereby maintaining the main satellite’s orbital motion in a stable state. Simulation results confirm the feasibility and effectiveness of the proposed mission-based method.