## 3 Results  
To verify the performance of the ADRC-ST-SMC (A-ST-SMC) algorithm proposed in this paper, it is applied to the attitude and position control system of a quadrotor UAV and evaluated through simulations using MATLAB 2024b. The simulation parameters are shown in Table 2 below. The control simulations are primarily carried out under random airflow disturbance conditions. The tracking ability of the control algorithm in the position direction is assessed by observing the UAV's position tracking and position error analysis. Additionally, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(m\) & UAV mass & 0.698 kg \\ \(l\) & Arm length & 0.171 m \\ \(G\) & Gravitational acceleration & 9.8 m/s\({}^{2}\) \\ \(I_{x}\) & Moment of inertia around X-axis & 0.0034 Kg\(\cdot\)m\({}^{2}\) \\ \(I_{y}\) & Moment of inertia around Y-axis & 0.0060 Kg\(\cdot\)m\({}^{2}\) \\ \(I_{z}\) & Moment of inertia around Z-axis & 1.302 \(\times\)10\({}^{-6}\) Kg\(\cdot\)m\({}^{2}\) \\ \(\beta_{1}\) & Gain coefficients & 30 \\ \(\beta_{2}\) & Gain coefficients & 300 \\ \(\beta_{3}\) & Gain coefficients & 1000 \\ \(k_{1}\) & Sliding mode gains & 20 \\ \(k_{p}\) & Proportional term in PID control & 2 \\ \(k_{d}\) & Derivative term in PID control & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: UAV body parameters. dynamic response performance and robustness of the control algorithm are validated by observing the UAV's attitude stability and attitude angle errors. Figures 6 and 7 below show the use of the Dryden turbulence model to simulate random airflow disturbances, which serves as the disturbance model for validating the proposed algorithm. The curve shown in Figure 8a represents the output curves of three control methods for the UAV. From the analysis of the curve, it can be concluded that at the beginning, the system experiences significant fluctuations due to the impact of disturbed airflow. Random airflow disturbance is introduced between 0 and 5 s. By observing the PID and ADRC control curves, it can be seen that these two control algorithms exhibit significant overshoot and oscillation in their output curves, indicating insufficient stability of the UAV's control under random airflow disturbances. However, by observing the A-ST-SMC, it is evident that the amplitude of its overshoot is significantly lower than that of the traditional PID and ADRC algorithms. Moreover, the oscillation frequency and magnitude of the curve are superior to those of PID and ADRC algorithms, reflecting that the A-ST-SMC control algorithm exhibits stronger robustness against random airflow disturbances, with the curve  
Figure 6: Position wind disturbance. Figure 7: Attitude wind disturbance. quickly returning to stability. After the final 5 s, all three control algorithms gradually stabilize. However, through comparison, it can be observed that the time for stabilization in A-ST-SMC is shorter than that in PID and ADRC. It can also be concluded that the performance of PID and ADRC under random airflow disturbance is inferior to that of A-ST-SMC. Figure (b)b shows the same curves as Figure (a)a. Figure (c)c shows the altitude trajectory response curve of the UAV. It can be observed that in the initial stage, from 0 to 5 s, after random airflow disturbance is introduced, the traditional control algorithms exhibit significant jitter and tracking errors with large jitter amplitudes. In comparison, the performance of the A-ST-SMC algorithm is clearly  
Figure 8: Experimental results of UAV trajectory tracking position. superior to PID and ADRC. From the figure, it is evident that both the jitter amplitude and the convergence speed of A-ST-SMC are much lower than those of PID and ADRC. After the UAV has completely stabilized, it is found that PID and ADRC always have errors with the reference value, while the output of A-ST-SMC completely coincides with the reference value.