M. A. Kudrov, K. D. Bukharov, E. A. Zakharov, D. R. Mahotkin, N. E. Krivoshein, N. A. Grishin, V. Semenkin Intelligent control algorithm for a group of unmanned aerial vehicles
M. A. Kudrov, K. D. Bukharov, E. A. Zakharov, D. R. Mahotkin, N. E. Krivoshein, N. A. Grishin, V. Semenkin Intelligent control algorithm for a group of unmanned aerial vehicles


This article covers the algorithm of group control of aircraft in a dynamically changing environment. The task of the group of unmanned aerial vehicles (UAVs) is (the as one type of search) independent search and destruction of enemy group of vehicles in a limited space with minimal loss. The fight against groups of small aircraft in a limited space is one of the problems that have arisen in recent years in relation to the development of small unmanned aerial vehicles. Currently, it is necessary to elaborate the theoretical and practical basis in the field of ground control of unmanned aerial vehicles for the successful solution of the tasks. In order to study of algorithms of group interaction the software stand modeling air fight was developed, and the modules realizing classical and adaptive algorithms of management were prepared. There are a description of the software stand and results of the studied algorithms in the article.


mathematical model, state assessment function, utility function, genetic algorithm, group control, optimization.

PP. 3-11.

DOI 10.14357/20718632190401


1. Novitzky P., Kokkeler B., Verbeek P. The Dual-use of Drones Tijdschrift voor Veiligheid 2018 (17) 1-2 doi: 10.5553/TvV/18727948201801710200
2. Bunker R. J. 2015. Terrorist and insurgent unmanned aerial vehicles: use, potentials, and military implications // Strategic Studies Institute and U. S. Army War College Press.
3. Kopytin V. 2018. Voyna dronov. Pochemu terroristy v Sirii vso chashche ispol'zuyut bespilotniki? [Why are terrorists in Syria increasingly using drones?] // LIFE. Available at:сирия/1076718/voina_ dronov_pochiemu_tierroristy_v_sirii_vsio_chashchie_ispolzuiut_biespilotniki (accessed June 09, 2019).
4. Petrosyan L.A., Rikhsiyev B.B. 1991. Presledovanie s prostym dvizheniyem [Chasing with a simple movement]. Moscow: The science. 96 p.
5. Ayzeks R. 1967. Differentsial'nyye igry [Differential Games]. Moscow: World. 480 p.
6. Majumdar D. 2017. U.S. Military Successfully Tested Its Latest Super Weapon: ‘The Swarm’ // The National Interest. Available at: (accessed June 09, 2019).
7. Zulu A., John S. A. 2014. Review of Control Algorithms for Autonomous Quadrotors // Open Journal of Applied Sciences. 10 p.
8. Khuwaja K., Lighari N., Tarca I. C., Tarca R. C. 2018. PID Controller Tuning Optimization with Genetic Algorithms for a Quadcopter // Recent Innovations in Mechatronics (RIiM) Vol. 5. (2018). No. 1.
9. Galvez R. L., Dadios E. P., Bandala A. A. 2014. Path Planning for Quadrotor UAV Using Genetic Algorithm // 7th IEEE International Conference Humanoid. 5 p.
10. Andrzej P., 2001. Fuzzy Modeling and Control. Physica-Verlag Heidelberg. 728 p.
11. Talha M., Asghar F., Rohan A., Rabah M., Kim S. H. 2018. Fuzzy Logic-Based Robust and Autonomous Safe Landing for UAV Quadcopter // Arabian Journal for Science and Engineering. 13 p.
12. Ernest N., Carroll D., Schumacher C., Clark M., Cohen K., Lee G. 2016. Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions // Journal of Defense Management. 7 p.
13. Vodolazskiy I. A., Yegorov A. S., Krasnov A. V. 2017. Royevoy intellekt i yego naiboleye rasprostranonnyye metody realizatsii [Swarm intelligence and its most common implementation methods]. // Young scientist. p. 147-153. Available at: June 09, 2019).
14. Ivanov D. Y. 2011. Metody royevogo intellekta dlya upravleniya gruppami malorazmernykh bespilotnykh letatel'nykh apparatov [roya intelligence methods for managing groups of small unmanned aircraft]. Izvestiya Southern Federal University. Technical science. 9 p.
15. Korevanov S. V., Kazin V. V. 2014. Iskusstvennyye neyronnyye seti v zadachakh navigatsii bespilotnykh letatel'nykh apparatov [Аrtificial neural networks in the problems of navigation of unmanned aircraft]. Scientific Bulletin of Moscow State Technical University of Civil Aviation. 4 p.
16. Amer K., Samy M., Shaker M., ElHelw M. 2019. Deep Convolutional Neural Network-Based Autonomous Drone Navigation // Center for Informatics Science. 8 p.
17. Hostmark J. B. 2007. Modelling Simulation and Control of Fixed-wing UAV: CyberSwan // Institutt for teknisk kybernetikk. 106 p.
18. Besekerskiy V. A., Popov Y. P. 2003. Teoriya sistem avtomaticheskogo upravleniya [Theory of automatic control systems]. Saint Petersburg: Profession. 752 p.
19. Petrov V. 2017. Manevrirovaniye v vozdushnom boyu. [Maneuvering in aerial combat]. Foreign military review Available at: (accessed June 09, 2019).
20. Podzorov S. Y. 2004 Kurs lektsii po teorii algoritmov NGU [Lecture course on the theory of algorithms of NSU].
21. Babich V. K. 1991. Vozdushnyy boy (zarozhdeniye i razvitiye) [Air combat (origin and development)]. Military publishing house. 95 p.
22. Rubinstein A. 2013. Lecture Notes in Microeconomic Theory. — 2nd. — Princeton University Press. — 153 p.— ISBN 978-0-691-15413-8.
23. Klimko Y. G. 2002. Geneticheskiy algoritm kak raznovidnost' evolyutsionnogo algoritma [genetic algorithm as a variety of evolutional algorithm]. Electronics and Informatics. 4 p.
24. Batishchev D.I., Neymark Y. A., Starostin N.V. 2007. Primeneniye geneticheskikh algoritmov k resheniyu zadach diskretnoy optimizatsii [Application of genetic algorithms to solving discrete optimization problems]. Educational material on the continuing education program "Information Technologies and Computer Modeling in Applied Mathematics" Nizhny Novgorod. 85 p.

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