## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Unit** & **Value** \\ \hline  & Simulation solver & & Ode3 (Bogacki-Shampine) \\ \hline
**dT** & Simulation step & s & 0.0001 \\ \hline
**m** & Drone mass & kg & 7 \\ \hline
**Wdir** & Wind direction & deg & \(-\)30 \\ \hline
**W\({}_{6}\)** & Wind speed at 6 m altitude & m/s & 7 \\ \hline
**W\({}_{500}\)** & Wind speed at 500 m altitude & m/s & 13.6 \\ \hline
**Astgt** & Target (cruise) airspeed & m/s & 20 \\ \hline
**X\({}_{0}\)** & Initial position & m & \([0,0,0]\) \\ \hline
**O\({}_{0}\)** & Initial orientation (roll, pitch, yaw) & deg & \([0,0,0]\) \\ \hline
**M\({}_{0}\)** & Mission point 0 (Takeoff point) & m & \([0,0,25]\) \\ \hline
**M\({}_{1}\)** & Mission point 1 & m & \([310,-80,25]\) \\ \hline
**M\({}_{2}\)** & Mission point 2 & m & \([1650,-610,500]\) \\ \hline
**M\({}_{3}\)** & Mission point 3 & m & \([2200,3450,500]\) \\ \hline
**M\({}_{4}\)** & Mission point 4 & m & \([-300,3000,200]\) \\ \hline
**M\({}_{5}\)** & Mission point 5 & m & \([-1400,2300,100]\) \\ \hline
**M\({}_{6}\)** & Mission point 6 (Deceleration point) & m & \([-1200,1500,100]\) \\ \hline
**M\({}_{7}\)** & Mission point 7 (Landing point) & m & \([-1200,1500,0]\) \\ \hline
**M\({}_{\text{p0}}\)** & Launch pause & s & 20 \\ \hline
**M\({}_{\text{r}}\)** & Point acceptance radius & m & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 9: States machine model. allow switching to the hold position during the flight. While the L1-based system has much higher speeds [6, \(-\)5] m/s and also allows switching to the hold position \"on the fly\". Meaning, for the L1-based system, the same transportation task can be accomplished with a much smaller mission, which will reduce flight costs in practice. Figure 10 presents the results of the mission flight simulation. There are 2 views shown: Figure 10a presents a 3D view, presenting both horizontal and vertical behavior. Figure 10b presents a 2D view, where the horizontal deviation can be observed. As can be seen, the linear PID-controlled system has significantly higher derivations from the mission route than the adaptive one. The highest horizontal derivations can be observed near mission point 3, where drone changes its heading and altitude with the wind in its back. Also, there is vertical steady-state error for the PID-based system near point 4, which occurred due to the long descent at high speed. It is obvious that the characteristics of the linear system are barely sufficient to accomplish the mission under the given conditions. Transient analysis of takeoff and landing transients is also necessary to compare the systems. For this purpose, the graphs of these transients are shown below in Figure 11.  
The Figure 11 presents drone flight trajectory in 3D (\(x,y,z\) axis) at the Figure 10a and 2D (\(x,y\) axis) at the Figure 10b. Comparing the response of the systems to the disturbance Vx, it can be noted that the transient process completes faster with the L1 controller: the setting time in this case is  
Figure 11: Transition process compared in the time domain: (**a**) launch and acceleration; (**b**) deceleration and landing. Figure 10: Mission flight trajectory: (**a**) 3D graph; (**b**) 2D graph. \(t_{sp}=4.1\) s, whereas for a system with a PID controller \(t_{sp}=11.2\) s, with significantly lower overshoot at vertical speeds: 54% for PID and 2% for L1. It has to be noted that adaptive L1 launch has worse characteristics--overshoot and setting time are higher. This means that the drone requires more distance for transition. Overall control system characteristics are presented in Table 4.  
Based on data of Table 4, it can be determined that the adaptive control system, based on L1 controllers, significantly outperforms the sliding PID control system: the max horizontal error \(e_{h}\) for L1 controller is almost 3.5 times lower, which might be very important in flight-area limited mountain-type missions.