## 5 Real-Time Experimental Results
### Set-Point Tracking with Disturbance and the Payload  
The results of set-point tracking with disturbance and the payload consist of two different parts. The first relates to payload disturbing, and the second applies to CPP. In this part, all controller structures are tested under disturbance. The payload is disturbed using a stick to implement the disturbances as external disturbances, directly impacting the system stability. After that, in the CPP scenario, the disturbances are generated by themselves as explained in the aforementioned above section. In Figure 14, the disturbance effects are seen on the x-axis. PID and T1-FPID give slow responses, and generally, they do not reach the reference points. IT2-FPID is not as affected as the others. In Figure 15, the biggest problem for all controller types is on the y-axis. The PID cannot guarantee stability under disturbance, and T1-FPID and IT2-FPID show a slight overshoot. Figure 14: Disturbance results on the x-axis. Figure 13: Set-point tracking results on the y-axis. After the disturbance-based results, some specific points are given for each controller. The second experiment of the IT2-FPID controller is coverage path planning. Each vertex of the coverage path planning is fixed and determined before the test, and these are given as reference points. The reference point changes to the next vertex when the UAV converges or reaches the current vertex. Considering the task compilation time, IT2-FPID, T1-FPID, and PID complete the task in 74.55 s, 112.35 s, and 120.15 s, respectively. In addition to a faster completion time, IT2-FPID also shows better results in other ways. The first experiment of the CPP is the PID controller structure for the real-time UAV with a flexible cable-connected payload as shown in Figure 16. PID shows a small steady-state error and slower system response on the x-axis. However, PID has a problem on the y-axis. It does not reach the reference points properly. In Figure 17, T1-FPID does not show promising results compared with the PID results on the x-axis, but it completes the task time faster than the PID controller; these results are plausible. However, T1-FPID oscillates on the y-axis. This issue can be explained by the fact that the payload is not at the CoM of the UAV but nearer the front side. Therefore, the problem on the y-axis is expected for all of the controller's structures. The last and the best results achieved with the IT2-FPID are shown in Figure 18. It completes the CPP task in 74.55 s. In addition, it has a better settling time, oscillation, and steady-state error in terms of minor errors and short time. Although the payload position problem on CoM exists for the other controllers, IT2-FPID overcomes the problem steadfastly. Figure 16: Coverage path planning with PID. Figure 15: Disturbance results on the y-axis. The experimental results on each axis are analysed separately. RMSE is chosen as a performance index for the comparison of the controllers. In Table 4, the results of RMSE on the x-axis are shown. The best results of each experiment are IT2-FPID on the x-axis. As far as the RMSE values obtained in the set-point tracking scenario and in the testing against the disturbance, that is, disturbing the payload with a stick, are considered, IT2-FPID shows almost two times better results when compared to PID and T1-FPID as given in Table 5.  
In summary, IT2-FPID has the best performance results in all two robustness tests. Considering the aim of the testing for IT2-FPID, the minimum task time, and RMSE values, maximum accuracy is obtained. However, although T1-FPID is not as good as IT2-FPID, it has better results than the compared PID controller. In the simulation, 20 independent tests are conducted, and then 5 independent tests are run in the real-time system.