Reinforcement learning in spacecraft control applications: Advances, prospects, and challenges

Autor: Massimo Tipaldi, Raffaele Iervolino, Paolo Roberto Massenio

This paper presents and analyzes Reinforcement Learning (RL) based approaches to solve spacecraft control problems. Different application fields are considered, e.g., guidance, navigation and control systems for spacecraft landing on celestial bodies

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3d0e141014379b40e9d97d081e866e7
https://hdl.handle.net/11588/908768

Adaptive satellite attitude control for varying masses using deep reinforcement learning.

Autor: Retagne, Wiebke, Dauer, Jonas, Waxenegger-Wilfing, Gunther, Condurache, Daniel, Zhang, Haibo

Publikováno v: Frontiers in Robotics & AI; 2024, p1-10, 10p

Integrated entry guidance with no-fly zone constraint using reinforcement learning and predictor-corrector technique.

Autor: Gao, Yuan, Zhou, Rui, Chen, Jinyong

Publikováno v: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering; Jun2024, Vol. 238 Issue 7, p728-741, 14p

SE(3) Based LTV-MPC Algorithm for Multi-Obstacle Trajectory Tracking of Fully Driven Spacecraft

Autor: Wenbang Wang, Xiaokang Peng, Jia Ai, Cheng Fu, Chaobo Li, Zejian Zhang

Publikováno v: IEEE Access, Vol 12, Pp 37850-37861 (2024)

A linear time-varying model predictive control (LTV-MPC) method based on $SE(3)$ is proposed, to enhance the control precision and reduce energy consumption of the spacecraft. First, left-invariant principle of Lie group, Lie algebra $SO(3)$ , and ot

Externí odkaz: https://doaj.org/article/3da97783e187432ca557f9a480c1801e

Online Condition Monitoring of Industrial Loads Using AutoGMM and Decision Trees.

Autor: Brescia, Elia, Vergallo, Patrizia, Serafino, Pietro, Tipaldi, Massimo, Cascella, Davide, Cascella, Giuseppe Leonardo, Romano, Francesca, Polichetti, Andrea

Publikováno v: Machines; Dec2023, Vol. 11 Issue 12, p1082, 18p