## 6 Conclusions and Outlook  
In this paper, the fuzzy neural network dynamic inverse controller is designed to solve the problems of poor attitude stability and difficult adjustment of the controller caused by the disturbance of atmospheric turbulence when quadrotor UAV performs tasks at sea level or complex terrain. Considering that atmospheric turbulence is a very complex physical phenomenon, the component model of atmospheric turbulence is firstly obtained based on the Dryden model. In the second chapter, the nonlinear dynamic model of UAV is established according to the Newton-Euler equation, and the atmospheric turbulence is added into the model as a disturbance. The third chapter adopts the method of dynamic inverse to cancel the nonlinear characteristics of the controlled object. The fuzzy neural network compensator is designed to eliminate the model inverse error caused by external disturbance. Finally, the designed controller is verified on the experimental platform, and in order to show its advantages, the PD-DIC method and the FA-DIC method are added to compare the effects. The experimental results show that the dynamic response performance of the FNN-DIC method and the stability in strong disturbance environment are better than the other two control methods.  
In future work, the impact of low-altitude wind shear on further UAVs needs to be further considered. The nonlinear model of quadrotor UAV will be decoupled and analyzed by combining time scale separation and state feedback. According to the motion, the state variables of the system are divided into multiple loops, and then the control laws between the loops are independently designed. In addition, actual test flight experiments will be conducted in a real environment.  
Conceptualization, B.C. and Z.Y.; methodology, B.C., Z.Y. and Y.W.; software, B.C., Z.Y. and C.L.; validation, B.C., Z.Y., Y.W., C.L. and P.L.; formal analysis, B.C. and Z.Y.; investigation, B.C.; resources, Z.Y.; data curation, B.C. and Z.Y.; writing--original draft preparation, B.C.; writing--review and editing, B.C., Z.Y. and Y.W.; visualization, B.C.; supervision, B.C. and Z.Y.; project administration, B.C. and Z.Y.; funding acquisition, B.C., Z.Y. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Focus on Research and Development Plan of Shandong Province of China (Major Scientific and Technological Innovation Project) under Grant 2022 CXGC010608,  
Figure 13: Yaw angle tracking results under atmospheric disturbance: (**a**) Yaw angle tracking curve, and (**b**) tracking error curve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel** & **PD-DIC** & **FA-DIC** & **FNN-DIC** \\ \hline Roll (\({}^{\circ}\)) & 0.098 & 0.013 & 0.006 \\ Pitch (\({}^{\circ}\)) & 0.625 & 0.127 & 0.032 \\ Yaw (\({}^{\circ}\)) & 0.047 & 0.011 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum tracking error of attitude angle under atmospheric turbulence.  
Natural Science Foundation of Shandong Province under Grant number No. ZR2022 MF252 and National Natural Science Foundation of China under Grant number No. (61803220, 62103219).  
The authors declare no conflict of interest.