The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF.

中文翻译：

---

通过自适应矢量场解决未建模的路径跟随动力学问题：无人机测试案例

无人机 (UAV) 基于模型的路径跟踪方法的实际性能显示出相当大的依赖于风知识和用于设计的动态模型的保真度。本研究分析并证明了自适应矢量场 (VF) 控制法则的性能，该法则可以弥补风矢量知识的缺乏和未建模的航向角动力学的存在。在商用固定翼无人机上校准并证明是现实的大量模拟实验表明，新的 VF 方法比其标准版本可以更好地应对不确定性。事实上，虽然标准 VF 方法非常适用于理想的一阶航向角动力学（以及风矢量的完美知识），但在存在未知风或未建模航向角动力学的情况下，其性能会下降。