Hostname: page-component-76d6cb85b7-lrvh5 Total loading time: 0 Render date: 2026-07-14T14:05:45.971Z Has data issue: false hasContentIssue false
  • English
  • Français

Research on the design of smart morphing long-endurance UAVs

Part of: Smart Aircraft International Symposium on Smart Aircraft 2019 Collection

Published online by Cambridge University Press:  25 September 2020

T. Ma ,
Y. Liu ,
D. Yang ,
Z. Zhang ,
X. Wang  and
S. Hao
T. Ma*
Affiliation:
Beijing Advanced Subject Center of Advanced Unmanned Aerial Vehicles, Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System, Ministry of Industry and Information Technology, Institute of Unmanned System, Beihang University, Beijing, China
Y. Liu
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
D. Yang*
Affiliation:
SATM, Cranfield University, Cranfield, UK
Z. Zhang
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
X. Wang
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
S. Hao
Affiliation:
School of Aeronautic Science and Engineering, Hiwing General Aviation Equipment Co. LTD, Beihang University, Beijing, China
*
matielin@buaa.edu.cn
tornado831001@hotmall.com
Get access Rights & Permissions [Opens in a new window]

Abstract

To improve the endurance performance of long-endurance Unmanned Aerial Vehicles (UAVs), a smart morphing method to adjust the UAV and flight mode continuously during flight is proposed. Using this method as a starting point, a smart morphing long-endurance UAV design is conducted and the resulting improvement in the endurance performance studied. Firstly, the initial overall design of the smart morphing long-endurance UAV is carried out, then the morphing form is designed and various control parameters are selected. Secondly, based on multi-agent theory, an architecture for the smart morphing control system is built and the workflow of the smart morphing control system is planned. The morphing decision method is designed in detail based on the particle swarm optimisation algorithm. Finally, a simulation of the smart morphing approach in the climb and cruise stages is carried out to quantitatively verify the improvement in the endurance performance. The simulation results show that the smart morphing method can improve the cruise time by 4.1% with the same fuel consumption.

Keywords

Smart morphingLong-endurance UAVEnduranceFuel consumption

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1283 , January 2021 , pp. 22 - 41
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Li, A., Shen, Y. and Zhang, W. The development of high altitude long-endurance UAV, Aeronaut Sci Technol., 2001, 2, pp 34–36.Google Scholar
Bai, P., Chen, Q. and Xu, G. Development status and prospect of key technologies of smart morphing aircraft, Chin. J. Aero., June 2019, 37, (3) pp 426–443.Google Scholar
Xu, Y. Research on the development and key technologies of smart morphing, Tactical Missile Technol., 2017, 2, pp 26–33.Google Scholar
Yin, G., Zhu, T. and Ren, P. Framework of electric vehicle chassis smart control system based on multi-Agent theory, Chin. Mech. Eng., August 2018, 29, (15), pp 1796–1801.Google Scholar
Eberhart, R. and Kennedy, J. A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. IEEE.Google Scholar
An, L., Long, T. and Huang, B. Fast optimization design of long-endurance UAV airfoil based on particle swarm optimization, J. Projectile Guid., June 2013, 33, (3), pp 119–122.Google Scholar
Elzey, D.M., Sofla, A.Y.N. and Wadley, H.N.G. A bio-inspired high-authority actuator for shape morphing structures, Smart Structures and Materials 2003: Active Materials: Behavior and Mechanics. International Society for Optics and Photonics, 2003.CrossRefGoogle Scholar
Wu, R, et al. A morphing aerofoil with highly controllable aerodynamic performance. Aeronaut. J., 2017, 121, (1235), pp 54–72.CrossRefGoogle Scholar
Guiler, R. and Huebsch, W. Wind tunnel analysis of a morphing swept wing tailless aircraft, AIAA: Applied Aerodynamics Conference, 2006.CrossRefGoogle Scholar
Hu, S. and Zhang, L. The theory, technology and application of multi-agent system, Comput. Sci., June 1999, 26, (9), pp 20–24.Google Scholar
Guan, X. and Li, Z. Research on aerodynamic shape CST parametric method, Acta Aeronautica et Astronautica Sinica, April 2012, 33, (4), pp 625–633.Google Scholar
Qin, D., Li, Z. and Wang, X. ROTAX912/914 series aero-engine introduction, Small Int. Combust. Eng. Veh. Technol., June 2017, 46, (3), pp 92–96.Google Scholar
Liu, X. Flight Performance and Planning, 2013Google Scholar
Zhang, Y., Luo, X. and Xiang, J. Calculation of flight range and flight time of propeller aircraft at constant altitude, Flight Dyn., December 2003, 21, (4), pp 30–34.Google Scholar
Li, H. Design and Research of Long-Endurance Morphing UAV Based on Smart Materials, 2016.Google Scholar
Anonymous Aeronautical Aerodynamic Manual, National Defense Industry Press, 1990, pp 720–828.Google Scholar
Van Ingen, J. The eN method for transition prediction. Historical review of work at TU Delft, 38th Fluid Dynamics Conference and Exhibit, 2008.CrossRefGoogle Scholar