To be able to recover a fixed-wing unmanned aerial vehicle (UAV) on a small space like a boat deck or a glade in the forest, a steep and precise descent is needed. One way to reduce the speed of the UAV during landing is by performing a deep-stall landing manoeuvre, where the lift of the UAV is decreased until it is unable to keep the UAV level, at the same time as the drag is increased to minimize the speed of the UAV. However, this manoeuvre is highly nonlinear and non-trivial to perform with high precision. To solve this, an on-line nonlinear model predictive controller (NMPC) is implemented to guide the UAV in the landing phase, receiving inputs from the autopilot and guiding the UAV using pitch and throttle references. The UAV is guided along a custom path to a predefined deep-stall landing start point and performs a guided deep-stall. The simulation results show that the NMPC guides the UAV in a deep-stall landing with good precision and low speed, and that the results depend on a correct prediction model for the controller.

为了能够在狭窄的空间（如森林中的船甲板或林间空地）上恢复固定翼无人机（UAV），需要陡峭而精确的下降。降低无人机着陆速度的一种方法是执行深度失速着陆操作，在这种情况下，无人机的升力会降低，直到无法保持无人机水平为止，同时增加阻力以最大程度地减小阻力无人机的速度。但是，这种机动是高度非线性的，并且对实现高精度而言并非易事。为了解决这个问题，在线非线性模型预测控制器（NMPC）被实施以在着陆阶段引导无人机，接收来自自动驾驶仪的输入，并使用俯仰和油门基准来引导无人机。无人机沿着自定义路径被引导到预定义的深度失速着陆起点，并执行引导性深度失速。