Methodology of multi-criteria evolutionary configuration formation for FPV-type UAV based on a prototype


Аuthors

Ananjev A. V.1, 2*, Akimov A. V.3, Kuziyarov N. F.2, Moiseev S. I.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. LLC «Radio Measurements», Kazan, Republic of Tatarstan
4. Voronezh State Technical University, VSTU, 14, Moskovsky prospect, Voronezh, 394026, Russia

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

Abstract

Developing a new unmanned aerial vehicle (UAV) from scratch requires significant resources, time, and financial investment. Instead, an optimal approach may be to modernize existing models. This work presents a methodology for the evolutionary formation of the configuration of FPV-type UAV based on a prototype, grounded in multi-criteria optimization. An analysis of existing methods for forming UAVs has been conducted. Based on this analysis, a basic model (prototype) of the UAV has been identified, and the elements included in the prototype are listed. The methodology encompasses a specific configuration and possesses certain characteristics that define the main properties of the product.
Multi-criteria optimization methods are used to solve problems where it is necessary to consider several conflicting criteria. The method of criterion constraints in multi-criteria optimization for the development of UAVs with specified characteristics allows for transforming a multi-criteria problem into a single-criteria one with a single objective function. This enables the use of standard analytical and numerical methods for finding optimal solutions. However, this feature is also characteristic of other multi-criteria optimization methods.
The decision to specifically use the criterion constraints method is primarily due to its ability to impose strict limits on each criterion that differs from the one being improved. This ensures that the parameters of the UAV do not fall below acceptable values within the set of Pareto-optimal solutions. Highlighting several key criteria during the implementation of the multi-criteria optimization method and sequential optimization for each of them allows for the characteristics of the configuration to be brought as close as possible to the required values.

Keywords:

formation of UAV, configuration of UAV, multi-criteria methodology, design, optimization, criteria constraints

References

  1. Efremov I.S., Strakhova A.O., Ryzhkova E.A. K voprosu o razrabotke konstruktsii bespilotnogo letatel'nogo apparata mul'tirotornogo tipa (On the Development of the Design of Multirotor-Type Unmanned Aerial Vehicles): sbornik nauchnykh trudov. Moscow: Izd-vo Rossiiskogo gosudarstvennogo universiteta im. A.N. Kosygina Publ., 2024. P. 179-186.
  2. Ronzhin A.L., Nguen V.V., Solenaya O.Ya. Analysis of the problems of unmanned flying manipulators development and UAV physical interaction with ground objects. Trudy MAI. 2018. No. 98. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=90439
  3. Karimov A.KH. Design features of new generation unmanned aerial vehicles (UAVs). Trudy MAI. 2011. No. 47. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=26769
  4. 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
  5. Trokhov D.A., Turkin I.K. On unmanned aircraft for reconnaissance missions performance upon the sea design procedure. Trudy MAI. 2014. No. 78. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=53735
  6. 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
  7. Guseinov O.A., Fatullaev A.A. Issues of designing power supply units for multicopters. Trudy MAI. 2024. No. 138. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=182666
  8. Zaitseva N.I., Pogarskaya T.A. Development of software complex for analysis and optimization of the aircraft assembly process. Trudy MAI. 2011. No. 124. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=167106. DOI: 10.34759/trd-2022-124-23
  9. Bodryshev A.V., Kuprikov M.Yu. Choice of the layout decision in the absence of an obvious prototype with factor application konkordacii. Trudy MAI. 2011. No. 47. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=26805
  10. Guseinov A.B. Methods of structural and parametric synthesis of structural and layout image UAV. Trudy MAI. 2011. No. 49. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=28120
  11. Il'in V.N., Lepekhin A.V. Technology of automation of structurally-parametrical synthesis on the basis of a method of a morphological box. Trudy MAI. 2011. No. 46. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=26008
  12. Gutkin L.S. Proektirovanie radiosistem i radioustroistv: uchebnoe posobie dlya radiotekhnicheskikh vuzov (Design of Radio Systems and Radio Devices: A Textbook for Radio Engineering Universities). Moscow: Radio i svyaz' Publ., 1986. 288 p.
  13. Gutkin L.S. Optimizatsiya radioelektronnykh ustroistv po sovokupnosti pokazatelei kachestva (Optimization of Radioelectronic Devices Based on a Set of Quality Indicators). Moscow: Sovetskoe radio Publ., 1975. 367 p.
  14. Anan'ev A.V., Zmii B.F., Kashchenko G.A. Modernization of Onboard Transceiver Systems for Unmanned Aerial Vehicles Based on an Evolutionary Approach. Radiotekhnika. 2016. No. 8. P. 46–49. (In Russ.)
  15. Kuzmenko R.V., Moiseev S.I., Stepanov L.V., Sysoeva T.P., Lukin A.N. Modeling the Solution of some Management Problems Using Latent Variables. Journal of Physics: Conference Series. 2021. V. 1902, P. 012076. DOI: 10.1088/1742-6596/1902/1/012076
  16. 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 upravleniya. 2015. No. 5. P. 58-66. (In Russ.)
  17. Shtoier R. Mnogokriterial'naya optimizatsiya: teoriya, vychisleniya i prilozheniya (Multi-Criteria Optimization: Theory, Computations, and Applications). Moscow: Radio i svyaz' Publ., 1992. 504 p.
  18. Podinovskii V.V., Nogin V.D. Pareto-optimal'nye resheniya mnogokriterial'nykh zadach (Pareto-optimal solutions of multi-criteria problems). Moscow: Nauka Publ., 1982. 250 p.
  19. Azarnova T.V., Barkalov S.A., Bondarenko Yu.V. et al. Matematicheskie metody prinyatiya reshenii. Klassicheskie podkhody i ikh razvitie: monografiya (Mathematical Methods for Decision Making. Classical Approaches and Their Development: monograph). Rostov-na-Donu: DGTU Publ., 2024. 205 p.
  20. Khomenyuk V.V. Elementy teorii mnogotselevoi optimizatsii (Elements of multi-objective optimization theory). Moscow: Nauka Publ., 1983. 124 p.
  21. Mashunin Yu.K. Metody i modeli vektornoi optimizatsii (Methods and models of vector optimization). Moscow: Nauka Publ., 1986. 140 p.


Download

mai.ru — informational site MAI

Copyright © 2000-2025 by MAI

 Вход