Multi-objective stochastical revision of piston-prop MUAV for maximization of autonomous performance,range,endurance and ceiling altitude

Abstract

In this research article the contribution of multi-objective stochastical revision of a mini unmanned aerial vehicle (i.e., MUAV) having liquid fuel piston-propeller powerplant system via redesigning its automatic flight control system (i.e., AFCS), “fuel weight/total weight” ratio, and propeller pitch for recovering both of autonomous flight performance and also general flight performance terms (i.e., maximum range, maximum endurance, and maximum ceiling altitude) is investigated. For this aim a piston-prop MUAV is manufactured in Ankara Yıldırım Bayezit University (i.e., AYBU) Drone Laboratory (i.e., DL) and named as piston-prop AYBU-MUAV. Some properties of this MUAV are as following: Its total mass is 2.2 kg, its wing span is 1.25 m, its wing aspect ratio is 6.25, and its powerplant is liquid-fuel powered two-stroke piston-prop engine. Its AFCS variables which are PID coefficients, its “fuel weight/total weight” ratio and its engine propeller pitch are simultaneously and stochastically revised with a multi-objective approach in order to maximize both autonomous flight performance and general flight performance via using a definite stochastic optimization methodology. Found revision fallouts are used meanwhile autonomous flight simulation of piston-prop MUAV. The first vital output of this research article is using simultaneous and stochastical revision approach during reaching optimum solutions of AFCS variables, “fuel weight/total weight” ratio, and engine propeller pitch. Another vital contribution of this research article is applying SPSA (i.e., simultaneous perturbation stochastical approximation) optimization policy for the previously stated aim. These significant contributions also reason much less fuel consumption and also green nature. Additionally, around %30 improvement in total cost index, %42 improvement in autonomous cost index and %12.6 improvement in general flight cost index (i.e., capturing terms relevant with range, endurance, and ceiling altitude) are found for this mentioned liquid fuel piston-prop MUAV with regard to the circumstance where simultaneous and stochastical revision idea is not benefitted.

Keywords

Piston-prop MUAV UAV revision simultaneous and stochastical revision range endurance ceiling altitude automatic flight control system performance

Get full access to this article

View all access options for this article.

References

Department of Defense, Office of the Secretary of Defense . FY2009–2034 unmanned systems integrated roadmap, DOD. Washington, DC: Department of Defense, 2009.

Austin

. Unmanned aircraft system. Hoboken, NJ: Wiley, 2010.

Bento

. Unmanned aerial vehicles: an overview. Red Bank, NJ: InsideGNSS. https://www.insidegnss.com/ (2008).

Grigoriadis

Skelton

. Minimum-energy covariance controllers. Automatica 1996; 3(4): 569–578.

Grigoriadis

Zhu

Skelton

. Optimal redesign of linear systems. J Dyn Syst Meas Control 1996; 118(3): 598–605.

Oktay

Sultan

. Simultaneous helicopter and control-system design. J Aircraft 2013; 50(3): 911–926.

Liu

Zhang

Jiao

, et al. Study on flight performance of propeller-driven UAV. Intern J of Aerospace Eng 2019; 2019: 1–11.

Gubran

AQA

Naef

AAQ

. Optimizing endurance and power consumption of fixed-wing unmanned aerial vehicles powered by proton-exchange membrane fuel cells. Arabian J Sci Eng 2024; 49: 2605–2623.

Benini

Chiereghin

. Turbofan multiobjective-multipoint optimization for UAV/UCAV applications. Aircraft Eng Aero Technol 2013; 85(5): 366–381.

10.

Zountouridou

Hatziargyriou

. A guide to unmanned aerial vehicles performance analysis—the MQ-9 unmanned air vehicle case study. J Eng 2023; 2023: 12270.

11.

Dinc

. Optimization of turboprop ESFC and NOx emissions for UAV sizing. Aircraft Eng Aero Technol 2017; 89(3): 357–383.

12.

. Research and application of mission planning for hollow long endurance UAV. Chengdu, China: Chengdu University of Electronic Science and Technology, 2013.

13.

Weinbergand

Wyzykowski

. Development and testing of a commercial turbofan engine for high altitude UAV applications. SAE Technical Paper 2001-01-2972. Warrendale, PA: SAE International, 2001.

14.

Jaegera

Adair

. Conceptual design of a high-endurance hybrid electric unmanned aerial vehicle. Mater Today Proc 2017; 4: 4458–4468.

15.

Cruzatty

Sarmiento

Valencia

, et al. Design methodology of a UAV propeller implemented in monitoring activities. Mater Today Proc 2024; 49: 481.

16.

Paw

Balas

. Development and application of an integrated framework for small UAV flight control development. Mechatronics 2011; 21: 789–802.

17.

Bradley

Parekh

. Comparison of design methods for fuel-cell-powered unmanned aerial vehicles. In: 26th applied AIAA conference. Honolulu, HI, 18–21 August 2009.

18.

Patrao

. On the aerodynamic design of the boxprop. Ph.D. dissertation, Dept. of Mech. and Maritime Sc. Gothenburg, Sweden: Chalmers University of Technology, 2019.

19.

Donateo

Ficarella

Spedicato

, et al. A new approach to calculating endurance in electric flight and comparing fuel cells and batteries. Appl Energy 2017; 187: 807–819.

20.

Sahin

Kose

Oktay

. Simultaneous autonomous system and powerplant design for morphing quadrotors. Aircraft Eng Aero Technol 2022; 94(8): 1228–1241.

21.

Kose

Oktay

Ozen

. Simultaneous arm morphing quadcopter and autonomous flight system design. Aircraft Eng Aero Technol 2023; 95(10): 1624–1632.

22.

Kose

. Octorotor flight control system design with stochastic optimal tuning, deep learning and differential morphing. J Braz Soc Mech Sci Eng 2024; 46(6): 381.

23.

Uzun

. Flight control system design of UAV with wing incidence angle simultaneously and stochastically varied. Aircraft Eng Aero Technol 2024; 96(5): 715–725.

24.

Nelson

. Flight stability and automatic control. Chapters II to VI. 2nd ed. New York, NY: McGraw-Hill, 2007.

25.

Etkin

Reyd

. Dynamics of flight: stability and control, chapters II to VI. New York, NY: John Wiley & Sons, 1996.

26.

Hale

. Introduction to aircraft performance, selection, and design. Chapter 6: level flight in the vertical plane: piston-props. Hoboken, NJ: John Wiley & Sons, 1984.

27.

Marchman

. Aerodynamics and aircraft performance. Chapter 6: range and endurance. 3rd ed. Blacksburg, VA: University Libraries at Virginia Tech, 2004.

28.

Leishman

. Introduction to aerospace flight vehicles. embry riddle aeronautical university. Chapter 11: atmospheric flight performance: flight range and endurance. 2024.

29.

Oktay

Konar

Onay

, et al. Simultaneous small UAV and autopilot system design. Aircraft Eng Aero Technol 2016; 88(6): 818–834.

30.

Chao

Cao

Chen

. Autopilots for small fixed-wing unmanned aerial vehicles: a Survey. In: Paper presented at IEEE international conference on mechatronics and automation, Harbin, China, 5–8 August 2007.

31.

Song

Spall

Soh

, et al. Robust neural network tracking controller using simultaneous perturbation stochastic approximation. IEEE Trans Neural Network 2008; 19(5): 817–835.

32.

Sadegh

Spall

. Optimal random perturbations for multivariable stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans Automat Control 1998; 43(10): 1480–1484.

33.

Marcus

. Convergence of simultaneous perturbation stochastic approximation for non-differentiable optimization. IEEE Trans Aero Electron Syst 2003; 48(8): 1459–1463.