Year: 2024
Type: article
Abstract: Rocket recycling offers significant potential to reduce launch costs, increase launch frequency, and reduce space debris, as evidenced by SpaceX’s success with rocket relandings. Traditional model-based control methods face challenges in handling the complex, non-linear dynamics inherent in the rocket recycling process. Reinforcement learning (RL), by contrast, provides a flexible, data-driven approach which is better suited to managing these complicate dynamic conditions. In this paper, we investigate two research problems in RL-based rocket recycling control: 1) how the granularity of control variables (e.g. thrust force levels) impacts the control performance; 2) how the environmental interference during training affects the robustness of RL controller. Our evaluation results demonstrate that increasing the granularity of control variables significantly slows the convergence of RL-based controller to a stable state. Moreover, introducing environmental interference during training—such as wind disturbances—improves both the efficiency and robustness of the RL-based controller. Our findings provide insights into how the RL-based controller can be more effectively used for rocket recycling tasks and highlight considerations that can enhance its applicability and robustness in dynamic environments.
Author: Alvin Mou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405592802