## 5 Simulation Results  
In the above sections, the design of the control law of the attitude system of UAV is completed. Through the analysis and derivation of the inverse error compensation, the feasibility of the designed controller is demonstrated theoretically. In addition, this paper conducts experiments on the designed nonlinear dynamic inverse controller with fuzzy neural network compensation in the simulation environment. By observing the flight status of the aircraft in a calm environment and under different interf-erence conditions, the experimental results are evaluated to further verify the effectiveness of the controller. The relevant parameters of the aircraft used in this paper are given in Table 2.  
The stability control of the attitude system is extremely critical to keep the UAV flying smoothly. The rotation of the attitude angle under the action of strong coupling will also cause the corresponding displacement of UAV. It is required that the aircraft can fly according to the desired trajectory during the execution of the mission, so the role of the controller prevents errors from being generated. In order to highlight the superiority of the control system designed in this paper, it is compared with the experimental results using only conventional proportional-differential control and fuzzy adaptive control.  
When designing a proportional-differential dynamic inverse controller (PD-DIC), first take approximate values of \(P\) and \(D\) according to relevant experience. Next, according to the principle of \(P\) and \(D\) tuning, the parameters are adjusted many times through experiments. Finally, combined with the simulation results, the parameters are adjusted by trial and error until the best. The roll channel adjustment parameters are \(k_{p}=5\), \(k_{d}=2\), pitch channel  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Parameter & Unit & Value & Parameter & Unit & Value \\ \hline \(m\) & kg & 1.5 & \(I_{Y}\) & kg \(\cdot\) m\({}^{2}\) & \(2.372\times 10^{-3}\) \\ \(l\) & m & 0.4 & \(I_{Z}\) & kg \(\cdot\) m\({}^{2}\) & \(6.316\times 10^{-2}\) \\ \(g\) & m/s\({}^{2}\) & 9.81 & \(k_{t}\) & & \(3.23\times 10^{-5}\) \\ \(l_{X}\) & kg \(\cdot\) m\({}^{2}\) & \(2.372\times 10^{-3}\) & \(k_{d}\) & & \(8.1\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aircraft parameters.  
Figure 4: Attitude control structure diagram of system.  
adjustment parameters are \(k_{p}=25,k_{d}=12\), and yaw channel adjustment parameters are \(k_{p}=20,k_{d}=6\).  
The fuzzy adaptive dynamic inverse controller (FA-DIC) takes the error \(e\) between the actual value and the expected value of the current attitude angle and its rate of change \(ec\) as the input, and the output are control parameters \(k_{1}\) and \(k_{2}\). The fuzzy subsets of these four parameters are all \([NB,NM,NS,ZO,PS,PM,PB]\), which are defined as \"negative large, negative medium, negative small, zero, positive small, positive medium, positive large\". Suppose the input fuzzy universe is \(X_{i}=[-r_{i},r_{i}](i=e,ec)\) and the output fuzzy universe is \(\gamma_{z}=[-c_{z},c_{z}](z=k_{1},k_{2})\), where \(r=3\), \(c_{k_{1}}=0.3\), and \(c_{k_{2}}=3\). Taking the pitch channel as an example, the three-dimensional curve diagram of the control parameters is shown in Figure 5.