不确定性下固定翼无人机基于安全学习的自适应增广控制
Safe Learning-Based Adaptive Augmentation Control for Fixed-Wing UAV under Uncertainty
- German Aerospace Center (DLR), Institute of Flight Systems（德国航空航天中心）
- CRAN, CNRS, Universite de Lorraine（洛林大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于强化学习的自适应增广控制方法，结合域随机化、安全滤波器和扰动观测器，补偿固定翼无人机系统中的匹配不确定性，并避免增广与安全滤波器的交互问题。
AI中文摘要：
本文提出了一种基于学习的自适应增广控制概念，其灵感来源于传统自适应控制的适应机制，但不受其特定参数自适应结构的限制。与用经典自适应控制增强强化学习（RL）基线控制器以弥补仿真到现实差距的方法不同，所提方法采用基于RL的自适应增广来解决传统自适应控制的局限性。采用域随机化结合观测堆叠来训练基于RL的增广，以补偿固定翼飞机系统中的匹配不确定性。为确保运行期间的约束满足，控制架构中集成了安全滤波器。基于伪控制对冲（PCH）的概念，我们提出了一种改进的参考模型，以避免基于RL的增广与安全滤波器之间的不良交互。为降低安全滤波器的保守性，我们额外引入了扰动观测器。所提方法在存在不确定性的固定翼飞机模型上进行了评估。
英文摘要：
This paper presents a learning-based adaptive augmentation control concept inspired by the adaptation mechanisms of conventional adaptive control, while not being restricted to their specific parametric adaptation structures. In contrast to augmenting a reinforcement learning (RL) baseline controller with classical adaptive control to account for the simulation-to-reality gap, the proposed approach uses RL-based adaptive augmentation to address the limitations of conventional adaptive control. Domain randomization combined with observation stacking is employed to train the RL-based augmentation to compensate for matched uncertainties in a fixed-wing aircraft system. To ensure constraint satisfaction during operation, a safety filter is incorporated into the control architecture. Based on the concept of pseudo control hedging (PCH), we propose a modified reference model that avoids undesirable interactions between the RL-based augmentation and the safety filter. To reduce the conservatism of the safety filter, we additionally incorporate a disturbance observer. The proposed approach is evaluated on a fixed-wing aircraft model subject to uncertainties.
