Safe Learning-Based Adaptive Augmentation Control for Fixed-Wing UAV under Uncertainty

Abstract

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.