An investigation of the control quality of the automatic control system for fixed-wing UAVs during landing process

Abstract

This study presents an investigation and evaluation of the control quality of the automatic control system for UAVs in the vertical plane under windy conditions. For the operational stages of UAVs in general, the landing stage is one of the high-probability stages that pose a threat to flight safety, especially at the time of landing. Therefore, to evaluate the control quality of the system, the authors investigated the parameters during UAV landing. The automatic control system uses a PID controller with optimal parameters selected by the Signal Constraint tool in Matlab Simulink. The predetermined wind model was used to verify at the most extreme times. The programs proposed in the paper are simulated on Matlab Simulink software.

Keywords

• UAV
• PID controller
• Automatic control system
• Landing approach

References

1. [1] L. Tan, J. Wu, X. Yang, and S. Song, “Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel,” Appl. Sci., vol. 9, no. 18, 2019, doi: 10.3390/app9183708.
2. [2] N. V. Kim, N. E. Bodunkov, I. G. Krylov, and E. A. Fedyaeva, “Selecting a flight path of an UAV to the ship in preparation of deck landing,” Indian J. Sci. Technol., vol. 9, no. 46, 2016, doi: 10.17485/ijst/2016/v9i46/107504.
3. [3] V. Magnus, “Automatic Takeoff and Landing of Unmanned Fixed Wing Aircrafts : A Systems Engineering Approach,” Computer Engineering, Department of Electrical Engineering, Linköping University, 2016. [Online]. Available: http://liu.diva-portal.org/smash/get/diva2:1055556/FULLTEXT01.pdf
4. [4] H. Bayerlein, P. De Kerret, and D. Gesbert, “Trajectory Optimization for Autonomous Flying Base Station via Reinforcement Learning,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018, pp. 1–5. doi: 10.1109/SPAWC.2018.8445768.
5. [5] J. Rohacs and N. D. Dung, “Robust planning the landing process of unmanned aerial vehicles,” Int. J. Sustain. Aviat., vol. 5, no. 1, p. 1, 2019, doi: 10.1504/ijsa.2019.10021483.
6. [6] P. Moriarty, R. Sheehy, and P. Doody, “Neural networks to aid the autonomous landing of a UAV on a ship,” in 2017 28th Irish Signals and Systems Conference (ISSC), 2017, pp. 1–4. doi: 10.1109/ISSC.2017.7983613.
7. [7] Y. Feng, C. Zhang, S. Baek, S. Rawashdeh, and A. Mohammadi, “Autonomous Landing of a UAV on a Moving Platform Using Model Predictive Control,” Drones, vol. 2, no. 4, 2018, doi: 10.3390/drones2040034.
8. [8] J. L. Sanchez-Lopez, S. Saripalli, P. Campoy, J. Pestana, and C. Fu, “Toward visual autonomous ship board landing of a VTOL UAV,” in 2013 International Conference on Unmanned Aircraft Systems (ICUAS), 2013, pp. 779–788. doi: 10.1109/ICUAS.2013.6564760.

9. [9] V. H. Quang, Synthesis of the on-board edge motion control system for UAV. 2008.
10. [10] C. V. Dang, T. P. Le, D. T. Nguyen, and M. T. Le, “Application of adaptive controller improves flight safety for small UAVs in wind turbulence conditions,” J. Mil. Sci. Technol., 2016.
11. [11] P. A. P. Thi, V. Nguyen, and T. L. Phan, “Path following algorithm for UAV,” J. Mil. Sci. Technol., 2018.
12. [12] V. T. Ngo, Xuan Can Nguyen, N. D. Nguyen, and H. S. Tran, “Optimizing the landing trajectory of the UAV in short runway conditions,” J. Mil. Sci. Technol., 2019.
13. [13] V. T. Ngo, X. C. Nguyen, T. P. Le, V. T. Nguyen, and C. V. Dang, “Using PI controller to track landing trajectory for small UAV,” J. Mil. Sci. Technol., 2020.
14. [14] V. T. Do, Q. T. Do, and V. T. Ngo, “Identify drone landing data on image processing database,” J. Sci. Eng., 2016.
15. [15] H. S. Tran, D. C. Nguyen, T. P. Le, and C. Van Nguyen, “Development of Automatic Landing Control Algorithm for Fixed-Wing UAVs in Longitudinal Channel in Windy Conditions,” in Modern Mechanics and Applications. Lecture Notes in Mechanical Engineering., 2022, pp. 945–958. doi: 10.1007/978-981-16-3239-6_74