Future space missions require technological advances to meet more stringent requirements. Next generation guidance, navigation, and control systems must safely operate autonomously in hazardous and uncertain environments. While these developments often focus on flight software, spacecraft hardware also creates computational limitations for onboard algorithms. Intelligent control methods combine theories from automatic control, artificial intelligence, and operations research to derive control systems capable of handling large uncertainties. While this can be beneficial for spacecraft control, such control systems often require substantial computational power. Recent improvements in single board computers have created physically lighter and less power-intensive processors that are suitable for spaceflight and purpose built for machine learning. In this study, we implement a reinforcement learning based controller on NVIDIA Jetson Nano hardware and apply this controller to a simulated Mars powered descent problem. The proposed approach uses optimal trajectories and guidance laws under nominal environment conditions to initialise a reinforcement learning agent. This agent learns a control policy to cope with environmental uncertainties and updates its control policy online using a novel update mechanism called Extreme Q-Learning Machine. We show that this control system performs well on flight suitable hardware, which demonstrates the potential for intelligent control onboard spacecraft.

未来的太空任务需要技术进步来满足更严格的要求。下一代制导、导航和控制系统必须在危险和不确定的环境中安全地自主运行。虽然这些发展通常集中在飞行软件上，但航天器硬件也对机载算法造成了计算限制。智能控制方法结合了自动控制、人工智能和运筹学的理论，推导出能够处理大不确定性的控制系统。虽然这对航天器控制可能是有益的，但这种控制系统通常需要大量的计算能力。单板计算机的最新改进创造了物理上更轻、功耗更低的处理器，适用于航天和机器学习。在这项研究中，我们在 NVIDIA Jetson Nano 硬件上实现了一个基于强化学习的控制器，并将该控制器应用于模拟的火星动力下降问题。所提出的方法使用标称环境条件下的最优轨迹和引导法则来初始化强化学习代理。该代理学习控制策略以应对环境不确定性，并使用称为 Extreme Q-Learning Machine 的新型更新机制在线更新其控制策略。我们展示了该控制系统在适合飞行的硬件上表现良好，这证明了在航天器上进行智能控制的潜力。所提出的方法使用标称环境条件下的最优轨迹和引导法则来初始化强化学习代理。该代理学习控制策略以应对环境不确定性，并使用称为 Extreme Q-Learning Machine 的新型更新机制在线更新其控制策略。我们展示了该控制系统在适合飞行的硬件上表现良好，这证明了在航天器上进行智能控制的潜力。所提出的方法使用标称环境条件下的最优轨迹和引导法则来初始化强化学习代理。该代理学习控制策略以应对环境不确定性，并使用称为 Extreme Q-Learning Machine 的新型更新机制在线更新其控制策略。我们展示了该控制系统在适合飞行的硬件上表现良好，这证明了在航天器上进行智能控制的潜力。