## 5 Simulation Results
**Scenario 3**: In the gust-disturbed environment of Scenario 2, UAV Motors 1-4 were further injected with specified fault values via the UAV's fault-tolerant control system, corresponding to the gain fault and deviation fault data shown in Table 1. At the same time, an additional 80 dB Gaussian white noise disturbance was added to the fault data during the experiment. Figure 6: Prediction results of ST\(-\)RBF and conventional RBF. Figure 7: Prediction error of ST\(-\)RBF and conventional RBF. The experimental data for Scenario 1 are shown in Figures 8 and 9. Comparing the performance of the traditional ADRC and the ST-RBFNN ADRC controller, we observed that both the flight controller designed in this paper and the traditional self-impeding control were able to achieve good attitude tracking control. However, in the experiment, when the remote control offered a large change in control command, the adjustment time of traditional ADRC was longer and the tracking error was larger than that of the fault-tolerant flight controller, and resulted in the oscillation of aircraft attitude. Figures 10 and 11 show the effect of the control of the aircraft attitude during the gust disturbance in Scenario 2. In the test experiment, we simulated a gust disturbance lasting 7 s, with a wind speed of 3-7 m/s on the aircraft at 14 s. A gust disturbance lasting 4 s with a wind speed of 5-9 m/s was simulated for the aircraft at 43 s. We observed that the aircraft based on active disturbance rejection control preserved good tracking performance in the presence of disturbances. Table 2 shows the controller performance parameters obtained for a given unit step of signal perturbation in Scenario 1 and Scenario 2 experiments. The fault-tolerant controller designed in this paper exhibited more stable tracking performance, a shorter adjustment time, and less overshoot than the traditional active disturbance rejection controller. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Generation Time (s)** & **Gain Fault Value** & **Deviation Fault Values (pwm: 0-150)** \\ \hline
5 s & 5\% & 30 \\
13 s & 10\% & 50 \\
30 s & 15\% & 60 \\
40 s & 15\% & 100 \\
50 s & 20\% & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Actuator fault data of the quadrotor. Figure 8: Flight attitude of quadrotor in Scenario 1. Figure 10: Flight attitude when external interference occurred in Scenario 2. \begin{table}
\begin{tabular}{l c c c} \hline
**Controler** & **Settling Time** & **Overshoot** & **Angle Error** \\ \hline Traditional ADRC in Scenario 1 & 2.3 s & 12.3\% & 0.3\({}^{\circ}\) \\ ST-RBFNN ADRC in Scenario 1 & 1.5 s & 0\% & 0.04\({}^{\circ}\) \\ Traditional ADRC in Scenario 2 & 3.2 s & 15.6\% & 0.4\({}^{\circ}\) \\ ST-RBFNN ADRC in Scenario 2 & 2.2 s & 5.3\% & 0.12\({}^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Controller performance index. Figure 9: Flight attitude tracking error of quadrotor in Scenario 1. Figures 12 and 13 show the change in the attitude of the aircraft in Scenario 3 when the fault was injected. In the experiment, we injected a gain fault of 0.05 into Motor 1 at 5 s and a deviation fault of 30 into Motor 2. After 13 s, Motor 1 was injected with a gain fault of 0.1 and Motor 2 with a deviation fault of 50. At the 30 s mark, both Motors 1 and 2 were injected with a gain fault of 0.15 and a deviation fault of 60. After 40 s, Motors 1-3 were simultaneously injected with the fault values in Table 1. At 50 s, all four motors were simultaneously injected with the fault data from Table 1.  
Figure 11: Flight attitude tracking error when external interference occurred in Scenario 2. Figure 12: Flight attitude when actuator failure occurred in Scenario 3. Comparing the tracking effects of the fault-tolerant controller in Figures 12 and 13, it is clear that in the first two fault injections, the conventional active disturbance rejection controller achieved stable tracking control by treating the actuator faults as external disturbances.