## 4 Results and Discussion
We used Simulink Control Design for tuning PID gains in a Simulink model. The fuzzy function adjusts PID parameters during the auto-tune cycle to enable the process to reach a predetermined setpoint in the shortest time with minimum overshoot and undershoot during power-up or load disturbances. Table 3 shows the step change parameters: a roll angle of 30\({}^{\circ}\), a height of 5 m, and an airspeed of 1 meter per second, respectively. The PID controller improvements were as follows: proportional, 5.4448; integral, 0; and derivative, 4.6668. The low-fidelity (within 2%), medium-fidelity (within 2%), and high-fidelity (within 2%) responses for the roll angle with the auto-tuned controller represented 41.5%, 41.5%, and 41.5% improvement compared with the compensator, respectively. For the height or altitude of the quadcopter position, the corresponding low-, medium-, and high-fidelity responses for the automatically tuned controller compared with the manual compensator were 11%, 11%, and 17.4%, respectively, while for the quadcopter's airspeed, they were 44%, 2%, and 2%, respectively. These improvements show that the automatically tuned controller was the better option. The roll for the drone in low, medium, and high fidelity is compared to the current work parameters (Table 4), which are 35.31%, 39.45%, and 53.6%, respectively, over the prior research work parameters. The present work parameters are 7%, 7%, and 8%, respectively, higher than the previous study's work parameters for the drone's height in low, medium, and high fidelity. In comparison to the previous study's work parameters, the drone's  
Figure 7:6 Two-dimensional advanced agricultural drone path tracking. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Specification**} & \multicolumn{3}{c}{**Automatic Tuned Controller Response (Fuzzy PID)**} & \multicolumn{3}{c}{**Classic Controller Response (PID)**} & \multicolumn{3}{c}{**Automatic Tuned Controller over**} \\  & \multicolumn{3}{c}{**Low**} & \multicolumn{3}{c}{**Medium**} & \multicolumn{1}{c}{**High**} & \multicolumn{1}{c}{**Low**} & \multicolumn{1}{c}{**Medium**} & \multicolumn{1}{c}{**High**} & \multicolumn{1}{c}{**Low**} & \multicolumn{1}{c}{**Medium**} & \multicolumn{1}{c}{**High**} \\  & **Fidelity** & **Fidelity** & **Fidelity** & **Fidelity** & **Fidelity** & **Fidelity** & **Fidelity** & **Fidelity** & **Fidelity** \\ \hline Roll & 1.55 s & 1.55 s & 1.55 s & 2.65 s & 2.65 s & 2.65 s & 41.5\% & 41.5\% & 41.5\% \\ \hline Height & 3.55 s & 3.55 s & 2.8 s & 4.0 s & 5.0 s & 4.6 s & 11 & 11 & 17.4 \\ \hline Airspeed & 0.55 s & 0.55 s & 0.55 s & 0.68 s & 0.98 s & 0.98 s & 44 & 44 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of responses (within 2%) obtained with the manual compensator-tuned controller and the automatically tuned controller at low, medium and high fidelity. airspeed in low, medium, and high fidelity is 72.08%, 41.22%, and 43.88%, respectively, under the present work conditions. Table 5 shows the obtained results of the state characteristic responses. The obtained results indicate a percentage change for the fuzzy PID controller over the PID controller of 90%, 35%, 56.67%, and 72.22% for overshoot, settling time, rise time, and steady state error, respectively. The percentages of change for the fuzzy PID controller over the fuzzy controller were 80% for overshoot, 8.23% for settling time, 33.67% for rise time, and 50% for steady state error. Therefore, combined controller schemes demonstrated the best performances.