具有预设性能的对接机动强化学习
Reinforcement Learning for Docking Maneuvers with Prescribed Performance
AI总结：
提出双组件数据驱动控制器，结合强化学习与附加反馈律及驻留时间激活方案，保障卫星对接机动在学习阶段的安全性与预设性能，并通过数值验证其避碰有效性。
摘要：
我们提出一种双组件数据驱动控制器，以安全执行卫星对接机动。利用强化学习基于测量数据推导最优控制策略。为保障学习阶段安全，控制单元中实现了附加反馈律，确保系统演化维持在预设性能界限内。我们定义了安全与安全关键区域，以基于实际测量训练反馈控制器。为避免抖振，实现了驻留时间激活方案。我们为所提出控制器在带避碰的卫星对接机动中的性能提供了数值证据。
英文摘要：
We propose a two-component data-driven controller to safely perform docking maneuvers for satellites. Reinforcement Learning is used to deduce an optimal control policy based on measurement data. To safeguard the learning phase, an additional feedback law is implemented in the control unit, which guarantees the evolution of the system within predefined performance bounds. We define safe and safety-critical areas to train the feedback controller based on actual measurements. To avoid chattering, a dwell-time activation scheme is implemented. We provide numerical evidence for the performance of the proposed controller for a satellite docking maneuver with collision avoidance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
