Hostname: page-component-669899f699-tpknm Total loading time: 0 Render date: 2025-04-25T20:35:26.891Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Published online by Cambridge University Press:  15 November 2024

H. Li Open the ORCID record for H. Li [Opens in a new window]
H. Li*
Affiliation:
Faculty of Air Navigation, Electronics and Telecommunications, National Aviation University, Kyiv, Ukraine
*
Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

Keywords

unmanned dronessatellite navigationautonomous operationvariable environmental conditionsautopiloting
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1332 , February 2025 , pp. 282 - 295
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S. and Mansoor, W. A comprehensive review of recent research trends on unmanned aerial vehicles (UAVs), Systems, 2023, 11, (8), p 400.CrossRefGoogle Scholar
Quamar, M.M., Al-Ramadan, B., Khan, K., Shafiullah, M. and El Ferik, S. Advancements and applications of drone-integrated geographic information system technology – a review, Remot. Sens., 2023, 15, (20), p 5039.CrossRefGoogle Scholar
Guo, K., Ye, Z., Liu, D. and Peng, X. UAV flight control sensing enhancement with a data-driven adaptive fusion model, Reliabil. Engin. Syst. Safet., 2021, 213, p 107654.CrossRefGoogle Scholar
Lee, M.T., Chuang, M.L., Kuo, S.T. and Chen, Y.R. UAV swarm real-time rerouting by edge computing D* lite algorithm, Appl. Sci., 2022, 12, (3), p 1056.CrossRefGoogle Scholar
Qu, C., Calyam, P., Yu, J., Vandanapu, A., Opeoluwa, O., Gao, K., Wang, S., Chastain, R. and Palaniappan, K. DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics, Futur. Gener. Comp. Syst., 2021, 125, pp 247–262.CrossRefGoogle Scholar
Hussein, M., Nouacer, R., Corradi, F., Ouhammou, Y., Villar, E., Tieri, C. and Castiñeira, R. Key technologies for safe and autonomous drones, Microproces. Microsyst., 2021, 87, p 104348.CrossRefGoogle Scholar
Kondratenko, Y.P., Klymenko, L.P. and Sidenko, I.V. Comparative analysis of evaluation algorithms for decision-making in transport logistics, Stud. Fuzz. Soft. Comp., 2014, 312, pp 203–217.CrossRefGoogle Scholar
Hysa, A. A study of the nonlinear dynamics inside the exoplanetary system Kepler-22 using MATLAB® software, EUREKA. Phys. Eng., 2024, 2024, (2), pp 3–12.CrossRefGoogle Scholar
Radzki, G., Nielsen, I., Golińska-Dawson, P., Bocewicz, G. and Banaszak, Z. Reactive UAV fleet’s mission planning in highly dynamic and unpredictable environments, Sustainability, 2021, 13, (9), p 5228.CrossRefGoogle Scholar
Falko, A., Gogota, O., Yermolenko, R. and Kadenko, I. Analysis of LArTPC data using machine learning methods, J. Phys. Stud., 2024, 28, (1), p 1802.CrossRefGoogle Scholar
Yang, M., Zhou, Z. and You, X. Research on trajectory tracking control of inspection UAV based on real-time sensor data, Sensors, 2022, 22, (10), p 3648.CrossRefGoogle ScholarPubMed
Zitar, R.A., Mohsen, A., Seghrouchni, A.E., Barbaresco, F. and Al-Dmour, N.A. Intensive review of drones detection and tracking: Linear Kalman filter versus nonlinear regression, an analysis case, Archiv. Comput. Method. Engin., 2023, 30, pp 2811–2830.CrossRefGoogle Scholar
Venturini, F., Mason, F., Pase, F., Chiariotti, F., Testolin, A., Zanella, A. and Zorzi, M. Distributed reinforcement learning for flexible and efficient UAV swarm control, IEEE Transact. Cognit. Commun. Network., 2021, 7, (3), pp 955–969.CrossRefGoogle Scholar
Quinones-Grueiro, M., Biswas, G., Ahmed, I., Darrah, T. and Kulkarni, C. Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios, Int. J. Prognost. Health Manag., 2021, 12, (3), pp 1–17.Google Scholar
Alam, M.S. and Oluoch, J. A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs), Expert Syst. Applic., 2021, 179, p 115091.CrossRefGoogle Scholar
Yermolenko, R., Klekots, D. and Gogota, O. Development of an algorithm for detecting commercial unmanned aerial vehicles using machine learning methods, Mach. Energ., 2024, 15, (2), pp 33–45.Google Scholar
Zuo, Z., Liu, C., Han, Q.L. and Song, J. Unmanned aerial vehicles: Control methods and future challenges, IEEE/CAA J. Automat. Sinic., 2022, 9, (4), pp 601–614.CrossRefGoogle Scholar
Babak, V.P., Babak, S.V., Eremenko, V.S., Kuts, Y.V., Myslovych, M.V., Scherbak, L.M. and Zaporozhets, A.O. Monitoring the air pollution with UAVs, Stud. Syst. Decis. Contr., 2021, 360, pp 191–225.CrossRefGoogle Scholar
Kharlamov, M.Y., Krivtsun, I.V., Korzhyk, V.N., Ryabovolyk, Y.V. and Demyanov, O.I. Simulation of motion, heating, and breakup of molten metal droplets in the plasma jet at plasma-arc spraying, J. Therm. Spr. Tech., 2015, 24, (4), pp 659–670.CrossRefGoogle Scholar
Nguyen, D.D., Rohacs, J. and Rohacs, D. Autonomous flight trajectory control system for drones in smart city traffic management, ISPRS Int. J. Geo-Inf., 2021, 10, (5), p 338.CrossRefGoogle Scholar
Biliuk, I., Shareyko, D., Savchenko, O., Havrylov, S., Mardziavko, V. and Fomenko, L. Tracking system of a micromanipulator based on a piezoelectric motor. In Proceedings of the 5th International Conference on Modern Electrical and Energy System, MEES 2023, Institute of Electrical and Electronics Engineers, 2023. https://doi.org/10.1109/MEES61502.2023.10402375 CrossRefGoogle Scholar
Azarov, O., Kolesnyk, I. and Krupelnitskyi, L. Digital generation system for analog signals, Inf. Technol. Comput. Eng., 2024, 59, (1), pp 54–61. https://doi.org/10.31649/1999-9941-2024-59-1-54-61 Google Scholar
Javaid, S., Saeed, N., Qadir, Z., Fahim, H., He, B., Song, H. and Bilal, M. Communication and control in collaborative UAVs: Recent advances and future trends, IEEE Transact. Intellig. Transport. Syst., 2023, 24, (6), pp 5719–5739.CrossRefGoogle Scholar
Han, J., Shi, Y., Zhang, G., Korzhyk, V. and Le, W.Y. Minimizing defects and controlling the morphology of laser welded aluminum alloys using power modulation-based laser beam oscillation, J. Manufact. Proces., 2022, 83, pp 49–59.CrossRefGoogle Scholar
Poudel, S. and Moh, S. Task assignment algorithms for unmanned aerial vehicle networks: A comprehensive survey, Vehicul. Commun., 2022, 35, p 100469.CrossRefGoogle Scholar
Lin, N., Liu, Y., Zhao, L., Wu, D.O. and Wang, Y. An adaptive UAV deployment scheme for emergency networking, IEEE Transact. Wirel. Commun., 2021, 21, (4), pp 2383–2398.CrossRefGoogle Scholar
Umyshev, D.R., Dostiyarov, A.M., Duisenbek, Z.S., Tyutebayeva, G.M., Yamanbekova, A.K., Bakhtyar, B.T. and Hristov, J. Effects of different fuel supply types on combustion characteristics behind group of V-gutter flame holders: Experimental and numerical study, Therm. Sci., 2020, 24, pp 379–391.CrossRefGoogle Scholar
Torepashovna, B.B., Kairbergenovna, M.A., Sergeyevich, K.M., Uyezbekovna, T.G. and Kairbekovna, Z.A. AP13068541 development of an experimental energy complex based on an upgraded boiler plant using biofuels. In 2022 International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022 – Proceedings, Virtual, Online, Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/CIEES55704.2022.9990656 CrossRefGoogle Scholar
Alam, M.M., Arafat, M.Y., Moh, S. and Shen, J. Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey, J. Netw. Comput. Appl., 2022, 207, p 103495.CrossRefGoogle Scholar
Chen, L., Liang, H., Pan, Y. and Li, T. Human-in-the-loop consensus tracking control for UAV systems via an improved prescribed performance approach, IEEE Transact. Aerospac. Electr. Syst., 2023, 59, (6), pp 8380–8391.CrossRefGoogle Scholar
Zogopoulos-Papaliakos, G., Karras, G.C. and Kyriakopoulos, K.J. A fault-tolerant control scheme for fixed-wing UAVs with flight envelope awareness, J. Intellig. Robot. Syst., 2021, 102, (2), p 46.CrossRefGoogle Scholar
Priya, P. and Kamlu, S.S. Robust control algorithm for drones. In Aeronautics-New Advances, IntechOpen, 2022.CrossRefGoogle Scholar