Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Aerospace

DOI: 10.3390/aerospace12030170

Authors: Shibo Shao, Dong Zhou, Guanghui Sun, Weizhao Ma, Runran Deng

Institutions: Harbin Institute of Technology, Chinese University of Hong Kong, Institute of Spacecraft System Engineering

Language: English

Topic: Spacecraft Dynamics and Control

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China