Achieving precise, highly-dynamic maneuvers with Unmanned Aerial Vehicles (UAVs) is a major challenge due to the complexity of the associated aerodynamics. In particular, unsteady effects -- as might be experienced in post-stall regimes or during sudden vehicle morphing -- can have an adverse impact on the performance of modern flight control systems. In this paper, we present a vortex particle model and associated model-based controller capable of reasoning about the unsteady aerodynamics during aggressive maneuvers. We evaluate our approach in hardware on a morphing-wing UAV executing post-stall perching maneuvers. Our results show that the use of the unsteady aerodynamics model improves performance during both fixed-wing and dynamic-wing perching, while the use of wing-morphing planned with quasi-steady aerodynamics results in reduced performance. While the focus of this paper is a pre-computed control policy, we believe that, with sufficient computational resources, our approach could enable online planning in the future.

翻译：实现无人机的高精度、高动态机动是航空领域的一大挑战，其关键在于相关空气动力学的复杂性。特别是非定常效应——例如失速后状态或突发机体变形中可能出现的现象——会对现代飞行控制系统的性能产生不利影响。本文提出一种涡粒子模型及其对应的基于模型控制器，能够对剧烈机动过程中的非定常气动特性进行推理。我们通过执行失速后栖停机动的变形翼无人机硬件平台对所提方法进行验证。结果表明：采用非定常气动模型可同时提升固定翼与动态翼栖停性能，而基于准定常气动规划的机翼变形则会导致性能下降。尽管本文聚焦于预计算控制策略，我们相信在充足计算资源支持下，该方法未来有望实现在线规划。