Methodology for the rational selection of a propeller-motor group for fpv-type uav based on latent variable theory


Аuthors

Ananjev A. V.1, 2*, Kuziyarov N. F.2**, Moiseev S. I.3, Pilkin S. P.4

1. Plekhanov Russian University of Economics, PRUE, 36, Stremyanny per., Moscow, 117997, Russia
2. Air force academy named after professor N.E. Zhukovskogo and Y. A. Gagarin, 54a Starye Bolshevikov str., Voronezh, 394064, Voronezh Region
3. Voronezh State Technical University, VSTU, 14, Moskovsky prospect, Voronezh, 394026, Russia
4. JSC «EREMEX», Moscow, Russia

*e-mail: Ananyev-Alexandr@yandex.ru
**e-mail: Kuziyarov@mail.ru

Abstract

The propeller-motor group is a fundamental component and an important functional element of FPV-type unmanned aerial vehicles making its rational selection during the design process highly relevant. The article analyzes existing approaches to the selection of propeller-motor group for multirotor unmanned aerial vehicles. Based on the results of the analysis, it was found that the choice of the propeller-motor group design option is complicated by the problem of resolving a set of contradictions between their individual technical characteristics. To resolve a set of contradictory requirements, the article suggests using the latent variables method. The latent variable method allows you to quickly evaluate alternatives when choosing a unmanned aerial vehicles according to qualitative criteria and has many advantages over other expert assessment methods, which allows it to be used in practice in a multi-criteria analysis of technical solutions at the initial stages of design.The authors emphasized the importance of rationally selecting the propeller-motor group to ensure the required speed, flight time, and the ability to carry the necessary payload. 
The analysis of each propeller-motor group parameter is carried out using the hierarchy analysis method and the latent variable method, and the advantages and disadvantages of these methods are listed.
The results of the research will help users of unmanned aerial vehicles make more rational decisions regarding the propulsion system, which, in turn, will enhance the efficiency and safety of unmanned aerial vehicles usage across various fields. By providing a structured approach to selecting the necessary equipment, users can ensure better performance and reliability in their operations. This thoughtful selection process is crucial for maximizing the potential of unmanned aerial vehicles technology.

Keywords:

propulsion system; expert evaluation; pairwise comparison method; latent variables; selection parameters for the propulsion system

References

  1. Efremov I.S., Strahova A.O., Ryzhkova E.A. On the Development of the Design of Multirotor-Type Unmanned Aerial Vehicles, Sbornik nauchnyh trudov kafedry avtomatiki i promyshlennoj jelektroniki Rossijskogo gosudarstvennogo universiteta im. A.N. Kosygina. Moskva, 2024, pp. 179-186.
  2. Petrov G.M., Tarasov N.V. Hardware Components for Transmitting Information Streams of Unmanned Systems, Metrologicheskoe obespechenie innovacionnyh tehnologij. Sbornik statej VI Mezhdunarodnogo foruma, Sankt-Peterburg, 2024, pp. 191-192.
  3. Zaben'kov I.I., Apanasjan M.I. Modeling a Digital Radio System for Transmitting Video Data from Unmanned Aerial Vehicles, Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radiojelektroniki, 2011, № 6 (60), pp. 74-78.
  4. Gogolev A.A. Semi-Natural Simulation of Multicopter-Type Unmanned Aerial Vehicles, Trudy MAI, 2017, № 92, at: www.mai.ru/science/trudy/
  5. Gusejnova R.O., Gumbatov D.A. Optimization of the conceptual design of unmanned aerial vehicles, Trudy MAI, 2024, № 136, at: www.mai.ru/science/trudy/
  6. Zajceva N.I., Pogarskaja T.A. Development of a Software Suite for Analyzing and Optimizing the Assembly Process in Aerospace Engineering, Trudy MAI, 2011, № 47, at: www.mai.ru/science/trudy/
  7. Bodryshev A.V., Kuprikov M.Ju. Selection of layout solutions in the absence of a clear prototype using the concordance coefficient, Trudy MAI, 2011, № 47, at: www.mai.ru/science/trudy/
  8. Gusejnov A.B. Methodology for structural-parametric synthesis of the design layout of an unmanned aerial vehicle, Trudy MAI, 2011, № 49. at: www.mai.ru/science/trudy/
  9. Il'in V.N., Lepehin A.V. Technology for automating structural-parametric synthesis based on the morphological box method, Trudy MAI, 2011, № 46. at: www.mai.ru/science/trudy/
  10. Gutkin L.S. Design of Radio Systems and Radio Devices: A Textbook for Radio Engineering Universities, M.: Radio i svjaz', 1986, pp. 288: il.; 22.
  11. Gutkin L.S. Optimization of Radioelectronic Devices Based on a Set of Quality Indicators, Moskva: Sov. radio, 1975, pp. 367: graf.; 22.
  12. Anan'ev A.V., Zmij B.F., Kashhenko G.A. Modernization of Onboard Transceiver Systems for Unmanned Aerial Vehicles Based on an Evolutionary Approach, Radiotehnika, 2016, № 8, pp. 46-49, at: https://mydrone.ru/fpv-spravochnik/?srsltid=AfmBOopVZxTbw FYnrRQMRpsqmBLF4A90COpI_fVghe3sqOqugUNVf8AP#vtx
  13. Gucykova S.V. Expert Evaluation Method. Teorija i praktika, M.: Kogito-Centr, 2011, pp. 888.
  14. Saati T., Kerns K.M. Analytical Planning. System Organization. Organizacija sistem: Per. s angl., M.: Radio i svjaz', 1991, pp. 224.
  15. Lootsma, F.A. Scale sensitivity in the multiplicative AHP and SMART, F.A. Lootsma. -J. Multi-Criteria Decision Analysis, V.2, 1993.
  16. Niu H., Korchagina E.V. Economic efficiency of digital technology application in energy companies: an analysis based on the analytic hierarchy process, Research Result. Business and Service Technologies, 2024, T. 10, № 2, pp. 175-184.
  17. Pospehov G.B., Savon Ju., Mosekin V.V. Landslide susceptibility zonation using the analytical hierarchy process: a case study of Guantanamo province, Mining Informational and Analytical Bulletin (Scientific and Technical Journal), 2024, № 1, pp. 125-145.
  18. Krajnjak M. Analytic hierarchy process in Czech taxpayers’ decision-making regarding their tax liability, Journal of Tax Reform, 2020, T. 6, № 2, pp. 142-156.
  19. Klimenko I.S. The method of hierarchy analysis in modeling the strategy of formation of innovative potential, Modern Science and Innovations, 2024, № 2 (46), pp. 17-25.
  20. Maslak A.A., Moiseev S.I. Rasch Model for Latent Variable Estimation and Its Properties. Monograph, Monografija, Voronezh: NPC «Nauchnaja kniga», 2016. pp. 177.
  21. Moiseev S.I., Kireev Ju.V., Goncharov S.V. Latent Variable Estimation Model with Continuous Sets of Input Data and Its Applications, Sistemy upravlenija i informacionnye tehnologii, 2014, T. 57, № 3.1. pp. 161-167.
  22. Moiseev S.I. Rasch Model for Latent Variable Estimation Based on the Least Squares Method, Jekonomika i menedzhment sistem upravlenija, 2015, № 2.1 (16), pp. 166-172.
  23. Maslak A.A. Investigation of measurement precision of latent variables depending on the noise test results, V sbornike: Society, integration, education. Proceedings of the International Scientific Conference. Rezekne Academy of Technology zglitibas, valodu un dizaina fakultate, 2019, pp. 422-429.
  24. Maslak A.A. Measurement of latent variable «creative self-efficacy» of students of chapter of Kuban State University at Slavyansk-on-Kuban, Journal Plus Education, 2019, T. 24, pp. 62-66.
  25. Barkalov S.A., Karpovich M.A., Moiseev S.I. Analytic Hierarchy Process: An Approach Based on Latent Variable Theory, Vestnik Juzhno-Ural'skogo gosudarstvennogo universiteta. Serija: Komp'juternye tehnologii, upravlenie, radiojelektronika, 2022, T. 22, № 2, pp. 58-66.
  26. Anan'ev A.V., Ivannikov K.S., Moiseev S.I. Application of Latent Variable Theory for Analyzing the Elemental Basis of Signal Processing Devices Using the Pairwise Comparison Method, Sistemy upravlenija i informacionnye tehnologii, 2022, № 3 (89), pp. 35-38.
  27. Maslak A.A., Moiseev S.I., Osipov S.A. Comparative Analysis of Parameter Estimates of the Rasch Model Obtained by Maximum Likelihood and Least Squares Methods, Problemy upravlenija, 2015, № 5. pp. 58-66.

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