# Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load
## 4 Simulation Verification
From Figures 10 and 11, it can be seen that the height control difference of the three algorithms is not significant, but from the height error policy graph, the error fluctuation of the neural network algorithm is significantly smaller than that of the adaptive algorithm, tracking slightly better. Figure 8: \(Y\)-axis change of a quadrotor carrying a load system: (**a**) weak interference; (**b**) strong interference. Figure 9: \(Y\)-axis error of a quadrotor carrying a load system: (**a**) weak interference; (**b**) strong interference. Table 4 below shows the comparison of the average error of different algorithms in height under different intensity interference. It is obvious from the table that the average error of the algorithm proposed in this paper is the smallest, and its performance is significantly improved compared to the integral backstepping algorithm and the backstepping algorithm. Figure 12 shows the attitude angles. The figure shows that the attitude angle section can follow the attitude response closely. It shows that the neural network adaptive integral backstepping controller can control the wind disturbance with almost no influence on the input control signal of the system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interference** & **Bks** & **Adp** & **Rbf** \\ \hline Weak & 0.0022 m & 0.0010 m & 0.0008 m \\ \hline Strong & 0.0047 m & 0.0016 m & 0.0011 m \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Z\)-position average error. Figure 10: \(Z\)-axis change of a quadrotor carrying a load system: (**a**) weak interference; (**b**) strong interference. In Figure 13, \(rbfibks\) denotes the neural network adaptive algorithm based on integral backstepping,the pendulum angle generated by the hanging load during the flight is shown. The black line indicates that the pendulum angle controller is not added, and the red line indicates that the pendulum angle controller is added. From the figures, it can be seen that the overshoot of the pendulum angle generated by adding the neural network pendulum controller is significantly smaller, and it is fast and stable with less oscillation, so that the pendulum angle of the hanging load is quickly stabilized at a smaller value, which proves that the proposed algorithm can quickly stabilize the pendulum angle while suppressing the disturbance. A comprehensive analysis of the above simulation results in MATLAB shows that after adding the neural network algorithm, a variety of performance indicators of the original algorithm can be improved. The comparison of simulation plots shows that the algorithm proposed in this paper has a higher overlap rate with the desired trajectory in terms of curve fit, and the stronger the wind interference, the more obvious the comparison  
Figure 12: An attitude angle change of a quadrotor carrying a load system: (**a**) weak interference; (**b**) strong interference. Figure 13: \(\alpha\), \(\beta\) swing angles of a quadrotor carrying a load system: (**a**) weak interference; (**b**) strong interference. effect of the algorithm, which also shows that the inclusion of the neural network has a better suppression effect on strong interference, proving that the problem proposed in the introduction can be effectively solved by the algorithm, which provides a reasonable basis for the completion of the thesis.