欠驱动卫星姿态控制的深度强化学习策略
Deep Reinforcement Learning Policies for Underactuated Satellite Attitude Control
AI总结：
本文研究了强化学习在卫星姿态控制中的应用，提出使用定制版 Proximal Policy Optimization 算法训练神经网络控制策略，实现了在标称及执行器故障的欠驱动情况下的卫星大角度机动，并在真实硬件上验证了其有效性和工业级指向精度。
摘要：
自主性是未来太空探索任务面临的关键挑战。深度强化学习有望开发出仅通过与环境的交互即可学习复杂行为的智能体。本文研究了强化学习在卫星姿态控制问题中的应用，即航天器相对于惯性参考系的角重新定向。在所提出的方法中，一组控制策略被实现为神经网络，并使用定制版本的 Proximal Policy Optimization 算法进行训练，以将小型卫星从随机起始角度机动至指定的指向目标。具体而言，我们针对两种工作条件解决该问题：标称情况，即所有执行器（一组3个反作用轮）均正常工作；以及欠驱动情况，即沿其中一轴随机模拟执行器故障。我们表明，智能体学会了有效地执行大角度姿态机动，具有快速收敛和工业标准的指向精度。此外，我们在代表性硬件上测试了所提出的方法，结果表明通过采取适当措施，在仿真中训练的控制器能够在真实系统中表现良好。
英文摘要：
Autonomy is a key challenge for future space exploration endeavours. Deep Reinforcement Learning holds the promises for developing agents able to learn complex behaviours simply by interacting with their environment. This paper investigates the use of Reinforcement Learning for the satellite attitude control problem, namely the angular reorientation of a spacecraft with respect to an in- ertial frame of reference. In the proposed approach, a set of control policies are implemented as neural networks trained with a custom version of the Proximal Policy Optimization algorithm to maneuver a small satellite from a random starting angle to a given pointing target. In particular, we address the problem for two working conditions: the nominal case, in which all the actuators (a set of 3 reac- tion wheels) are working properly, and the underactuated case, where an actuator failure is simulated randomly along with one of the axes. We show that the agents learn to effectively perform large-angle slew maneuvers with fast convergence and industry-standard pointing accuracy. Furthermore, we test the proposed method on representative hardware, showing that by taking adequate measures controllers trained in simulation can perform well in real systems.
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