Mathematical modeling and synthesis of optimal group high-altitude flight of unmanned aerial vehicles


Аuthors

Huseynov O. A.1, Guliyev F. F.2

1. National Aerospace Agency of Azerbaijan Republic, NASA, Baku, 8th mkr., Suleiman Sani Akhundov 1
2. Jihaz Production Association,

Abstract

One of the most important missions carried out with the help of UAVs is intelligence activities carried out in order to detect and identify objects of special interest. Increasing the efficiency, speed and efficiency of this mission can be achieved by organizing group flights of UAVs. At the same time, the reconnaissance activities of the UAV group must be carried out unnoticed by external observers and to fulfill this requirement, the UAV group must fly at high altitudes using highly sensitive reconnaissance equipment. High-altitude flights allow you to expand the coverage area of the explored area, which is an additional advantage of high-altitude flights. At the same time, when organizing high-altitude UAV flights, atmospheric conditions affecting aircraft should be taken into account, for example, factors such as temperature, pressure and air density. It is well known that air density depends on factors such as altitude, temperature, air pressure and relative humidity. The problem of mathematical modeling, optimization of the model and synthesis of the mode of operation of a group high-altitude flight of a UAV is formulated and solved in the sense of finding the minimum value of air density at which a group flight of a UAV is possible with an optimal relationship between the diameter of the propeller (screw) of the UAV and the torque. Calculating the functional dependence between these indicators, at which flight is possible at a certain altitude, allows you to determine those altitude intervals of UAV flights that correspond to the values of r_sr calculated from the specified intervals of torque values.

Keywords:

mathematical modeling, optimization, torque, UAV, group flight

References

  1. Zuo Z., Liu C., Han Q., Song J. Unmanned aerial vehicles: control methods and future challenges. IEEE/CAA Journal of Automatica Sinica. 2022. Vol. 9, No. 4. P. 1-14. DOI: 10.1109/JAS.2022.105410
  2. Alsawy A., Hicks A., Moss D., Mckeever S. An Image Processing Based Classifier to Support Safe Dropping for Delivery-by-Drone. 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS). Genova, Italy, 5–7 December 2022. DOI: 10.1109/IPAS55744.2022.10052868
  3. Harrington P., Ng W.P., Binns R. Autonomous Drone Control within a Wi-Fi Network. In Proceedings of the 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal, 20–22 July 2020, IEEE: Piscataway, NJ, USA, 2020. DOI: 10.1109/CSNDSP49049.2020.924
  4. Zinenkov YU.V., Lukovnikov A.V., Cherkasov A.N. Mathematical modeling of a power plant based on a turbofan engine for a high-altitude unmanned vehicle. Vestnik Kazanskogo gosudarstvennogo tekhnicheskogo universiteta im. A.N. Tupoleva. 2014. No. 4. P. 46-54. (In Russ.)
  5. Butov A.M., Kozarev L.A. Matematicheskoe modelirovanie rabochego protsessa aviatsionnykh dvigatelei (Mathematical modeling of working process in aviation engine). Moscow: VVIA im. N.E.Zhukovskogo Publ., 1993. 143 p.
  6. Gogolev A.A. Semi-natural modelling of unmanned aerial vehicles like multicopter. Trudy MAI. 2017. No. 92. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=77238
  7. Ogol'tsov I.I., Rozhnin N.B., Sheval' V.V. Development of mathematical model of spatial flight of a kvadrokopter. Trudy MAI. 2015. No. 83. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=62031
  8. Karimov A.KH. Main goals and tasks solved by unmanned aerial vehicles (UAVs). Trudy MAI. 2011. No. 47. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=26767
  9. Guseinova R.O., Gumbatov D.A. Optimization of the conceptual development of unmanned aerial vehicles. Trudy MAI. 2024. No. 136. (In Russ.). URL: https://trudymai.ru/ eng/published.php?ID=180684
  10. Kochkarov A.A. Modern engineering of small drones and features of their network interaction. Proektirovanie budushchego. Problemy tsifrovoi real'nosti. 2018. No. 1 (1). P. 113-121. (In Russ.). DOI: 10.20948/future-2018-17
  11. Timoshenko A.V., Baldychev M.T., Marenkov I.A., Pivkin I.G. Technique elaboration for various types of sources “suboptimal” monitoring routes by unmanned aerial vehicle. Trudy MAI. 2020. No. 111. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=115145. DOI: 10.34759/trd-2020-111-10
  12. Koshkarov A.S., Gulii D.D., Baryaksheva V.A. Multi-rotor unmanned aerial vehicle emergency landing algorithm based on underlying surface image analysis. Trudy MAI. 2023. No. 132. URL: https://trudymai.ru/eng/published.php?ID=176835
  13. Ziquan Yu, Youmin Zhang, Bin Jiang, et al. Fractional Order PID-Based Adaptive Fault-Tolerant Cooperative Control of Networked Unmanned Aerial Vehicles against Actuator Faults and Wind Effects with Hardware-in-the-Loop Experimental Validation. Control Engineering Practice. 2021. Vol. 114, P. 104861. URL: http://dx.doi.org/10.1016/j.conengprac.2021.104861
  14. Lei Cui, Ruizhi Zhang, Hongjiu Yang, Zhiqiang Zuo. Adaptive Super-Twisting Trajectory Tracking Control for an Unmanned Aerial Vehicle under Gust Winds. Aerospace Science and Technology. 2021. Vol. 115, P. 106833. URL: http://dx.doi.org/10.1016/j.ast.2021.106833 
  15. Riousset J.A., Pasko V.P., Bourdon A. Air‐density‐dependent model for analysis of air heating associated with streamers, leaders, and transient luminous events. Journal of Geophysical Research Atmospheres. 2010. Vol. 115, P. 12321. DOI: 10.1029/2010JA015918
  16. Ziquan Yu, Youmin Zhang, Bin Jiang, et al. Fractional Order PID-Based Adaptive Fault-Tolerant Cooperative Control of Networked Unmanned Aerial Vehicles against Actuator Faults and Wind Effects with Hardware-in-the-Loop Experimental Validation. Control Engineering Practice. 2021. Vol. 114, P. 104861. DOI: 10.1016/j.conengprac.2021.104861
  17. Dai X., Quan Q., Ren J., Cai K.Y. An analytical design-optimization method for electric propulsion systems of multicopter UAVs with desired hovering endurance. IEEE/ASME transactions on methatronics. 2019. Vol. 24, No 1. DOI: 10.1109/TMECH.2019.2890901
  18. Joshi D., Deb D., Muyeen M. Comprehensive review on electric propulsion system of unmanned aerial vehicles. Frontiers in energy research. 2022. Vol. 10. DOI: 10.3389/fenrg.2022.75201
  19. Cavcar M. The international standard atmosphere. Anadol u University, 2000. Turkey, Vol. 30, P. 9.
  20. Anton Kuzubov, Aaron Kates, David Western. Team KNSP. Design, construction and testing of humidity’s effect on atmospheric conditions. Technical Report. May 2017, Missouri University of Science and Technology, Rolla.


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