论文标题
限制的模仿学习，用于拍打翼无人机
Constrained Imitation Learning for a Flapping Wing Unmanned Aerial Vehicle
论文作者
论文摘要
本文介绍了微拍翼无人机的数据驱动的最佳控制政策。首先，基于动力学的几何公式计算一组最佳轨迹，该动力学的几何表述捕获了大角度拍打运动与准稳态空气动力学之间的非线性耦合。然后，根据模仿学习的框架将其转换为反馈控制系统。特别是，通过学习过程纳入了附加的约束，以增强由此产生的受控动力学的稳定性。与常规方法相比，所提出的约束模仿学习消除了在线生成其他最佳轨迹的需求，而无需牺牲稳定性。因此，计算效率大大提高。此外，这建立了第一个非线性控制系统，该系统稳定了触摸机翼航空车辆的耦合纵向和横向动力学，而无需依赖平均或线性化。这些由数值示例进行了说明，该示例的模拟模型灵感来自君主蝴蝶。
This paper presents a data-driven optimal control policy for a micro flapping wing unmanned aerial vehicle. First, a set of optimal trajectories are computed off-line based on a geometric formulation of dynamics that captures the nonlinear coupling between the large angle flapping motion and the quasi-steady aerodynamics. Then, it is transformed into a feedback control system according to the framework of imitation learning. In particular, an additional constraint is incorporated through the learning process to enhance the stability properties of the resulting controlled dynamics. Compared with conventional methods, the proposed constrained imitation learning eliminates the need to generate additional optimal trajectories on-line, without sacrificing stability. As such, the computational efficiency is substantially improved. Furthermore, this establishes the first nonlinear control system that stabilizes the coupled longitudinal and lateral dynamics of flapping wing aerial vehicle without relying on averaging or linearization. These are illustrated by numerical examples for a simulated model inspired by Monarch butterflies.
