Advertisement

Journal of Intelligent & Robotic Systems

, Volume 97, Issue 1, pp 109–124 | Cite as

Coordinate Descent Optimization for Winged-UAV Design

  • Haowei Gu
  • Ximin Lyu
  • Zexiang Li
  • Fu ZhangEmail author
Article

Abstract

In this paper, a powerful optimization framework is proposed to design highly efficient winged unmanned aerial vehicle (UAV) that is powered by electric motors. In the proposed approach, the design of key UAV parameters including both aerodynamic configurations, (e.g. wing span, sweep angle, chord, taper ratio, cruise speed and angle of attack) and the propulsion systems (e.g. propeller, motor and battery) are cast into an unified optimization problem, where the optimization objective is the design goal (e.g. flight range, endurance). Moreover, practical constraints are naturally incorporated into the design procedures as constraints of the optimization problem. These constraints may arise from the preliminary UAV shape and layout determined by industrial design, weight constraints, etc. The backend of the optimization based UAV design framework are highly accurate aerodynamic models and propulsion system models proposed in this paper and verified by actual experiment data. The optimization framework is inherently non-convex and involves both continuous variables (e.g. the aerodynamic configuration parameters) and discrete variables (e.g. propulsion system combinations). To solve this problem, a novel coordinate descent method is proposed. Trial designs show that the proposed method works rather efficiently, converging in a few iterations. And the returned solution is rather stable with different initial conditions. Finally, the entire approach is applied to design a quadrotor tail-sitter VTOL UAV. The designed UAV is validated by both CFD simulations and intensive real-world flight tests.

Keywords

Coordinate descent method Optimization based design UAV 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

Research supported by Hong Kong ITF Foundation (ITS/334/15FP).

References

  1. 1.
    Kuchemann, D.: The aerodynamic design of aircraft. Progress in aeronautical sciences, 1965, 6, 271 (Pergamon, London) (1978)Google Scholar
  2. 2.
    Niu, C.: Airframe Structural Design: Practical Design Information and Data on Aircraft Structures. Conmilit Press (1988)Google Scholar
  3. 3.
    Oates, G.C.: Aircraft propulsion systems technology and design Aiaa (1989)Google Scholar
  4. 4.
    Vatistas, G.H., Lin, S., Kwok, C.K.: Reverse flow radius in vortex chambers. AIAA J. 24(11), 1872, 1873 (1986).  https://doi.org/10.2514/3.13046 CrossRefGoogle Scholar
  5. 5.
    Goraj, Z., Cisowski, J., Frydrychewicz, A., Grendysa, W., Jonas, M.: Mini UAV design and optimization for long endurance mission. In: Proceedings of ICAS Congress (2008)Google Scholar
  6. 6.
    Gu, H., Lyu, X., Li, Z., Shen, S., Zhang, F.: Development and experimental verification of a hybrid vertical take-off and landing (vtol) unmanned aerial vehicle (UAV). In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 160–169. IEEE (2017)Google Scholar
  7. 7.
    Martins, J.R., Lambe, A.B.: Multidisciplinary design optimization: a survey of architectures. AIAA J. 51 (9), 2049–2075 (2013)CrossRefGoogle Scholar
  8. 8.
    Ebrahimi, M., Farmani, M.R., Roshanian, J.: Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization. Struct. Multidiscip. Optim. 44(6), 773–784 (2011)CrossRefGoogle Scholar
  9. 9.
    Hwang, J.T., Lee, D.Y., Cutler, J.W., Martins, J.R.: Large-scale multidisciplinary optimization of a small satellite’s design and operation. J. Spacecr. Rocket. 51(5), 1648–1663 (2014)CrossRefGoogle Scholar
  10. 10.
    Ashuri, T., Zaaijer, M.B., Martins, J.R., Van Bussel, G.J., Van Kuik, G.A.: Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy. Renew. Energy 68, 893–905 (2014)CrossRefGoogle Scholar
  11. 11.
    Zi, B., Ding, H., Cao, J., Zhu, Z., Kecskeméthy, A.: Integrated mechanism design and control for completely restrained hybrid-driven based cable parallel manipulators. J. Intell. Robot. Syst. 74(3-4), 643–661 (2014)CrossRefGoogle Scholar
  12. 12.
    Artoni, A.: A methodology for simulation-based, multiobjective gear design optimization. Mech. Mach. Theory 133, 95–111 (2019)CrossRefGoogle Scholar
  13. 13.
    Cramer, E.J., Dennis, Jr, J.E., Frank, P.D., Lewis, R.M., Shubin, G.R.: Problem formulation for multidisciplinary optimization. SIAM J. Optim. 4(4), 754–776 (1994)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Balling, R.J., Sobieszczanski-Sobieski, J.: Optimization of coupled systems-a critical overview of approaches. AIAA J. 34(1), 6–17 (1996)CrossRefGoogle Scholar
  15. 15.
    Braun, R., Gage, P., Kroo, I., Sobieski, I.: Implementation and performance issues in collaborative optimization. In: 6th Symposium on Multidisciplinary Analysis and Optimization, p. 4017 (1996)Google Scholar
  16. 16.
    Manning, V.M.: Large-scale design of supersonic aircraft via collaborative optimization (1999)Google Scholar
  17. 17.
    Dunning, P.D., Brampton, C.J., Kim, H.A.: Multidisciplinary level set topology optimization of the internal structure of an aircraft wing. In: 10th World Congress on Structural and Multidisciplinary Optimization, pp. 19–24 (2013)Google Scholar
  18. 18.
    Raymer, D.: Enhancing Aircraft Conceptual Design Using Multidisciplinary Optimization. Ph.D. thesis, Institutionen för flygteknik (2002)Google Scholar
  19. 19.
    Leifsson, L., Ko, A., Mason, W.H., Schetz, J.A., Grossman, B., Haftka, R.T.: Multidisciplinary design optimization of blended-wing-body transport aircraft with distributed propulsion. Aerosp. Sci. Technol. 25 (1), 16–28 (2013)CrossRefGoogle Scholar
  20. 20.
    Alonso, J.J., Colonno, M.R.: Multidisciplinary optimization with applications to sonic-boom minimization. Annu. Rev. Fluid Mech. 44, 505–526 (2012)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Antoine, N.E., Kroo, I.M.: Framework for aircraft conceptual design and environmental performance studies. AIAA J. 43(10), 2100–2109 (2005)CrossRefGoogle Scholar
  22. 22.
    Ganguli, R., Rajagopal, S.: Multidisciplinary design optimization of an UAV wing using kriging based multi-objective genetic algorithm. In: 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 17th AIAA/ASME/AHS Adaptive Structures Conference 11th AIAA No, p. 2219 (2009)Google Scholar
  23. 23.
    Batill, S.M., Stelmack, M.A., Yu, X.Q.: Multidisciplinary design optimization of an electric-powered unmanned air vehicle. Aircr. Des. 2(1), 1–18 (1999)CrossRefGoogle Scholar
  24. 24.
    Gu, H., Cai, X., Zhou, J., Li, Z., Shen, S., Zhang, F.: A coordinate descent method for multidisciplinary design optimization of electric-powered winged UAVs. In: 2018 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1189–1198. IEEE (2018)Google Scholar
  25. 25.
    Stengel, R.F.: Flight Dynamics. Princeton University Press (2015)Google Scholar
  26. 26.
    Zhang, X., et al.: Aircraft Design Manual, vol. VI. Aerodynamic design China Aviation Publishing & Media (2002)Google Scholar
  27. 27.
    Anderson, J.D.: Aircraft Performance and Design. McGraw-Hill Science/Engineering/Math (1999)Google Scholar
  28. 28.
    Drela, M.: Xfoil: An analysis and design system for low Reynolds number airfoils. In: Low Reynolds Number Aerodynamics, pp. 1–12. Springer (1989)Google Scholar
  29. 29.
    Gur, O., Mason, W.H., Schetz, J.A.: Full-configuration drag estimation. J. Aircr. 47(4), 1356–1367 (2010)CrossRefGoogle Scholar
  30. 30.
    Pennycuick, C.: Mechanics of flight. In: Avian Biology, vol. V, pp. 1–75. Elsevier (1975)Google Scholar
  31. 31.
    Paterson, J., MacWilkinson, D., Blackerby, W.: A survey of drag prediction techniques applicable to subsonic and transonic aircraft design. AGARD Aerodyn. Drag 38, SEE N 74-14709 06–01 (1973)Google Scholar
  32. 32.
    Mason, W.: Boundary layer analysis methods. Aerodynamic Calculation Methods for Programmable Calculators & Personal Computers (1981)Google Scholar
  33. 33.
    Shevell, R.S.: Fundamentals of flight (1989)Google Scholar
  34. 34.
    Torenbeek, E.: Synthesis of Subsonic Airplane Design. Springer, Delft (1982)CrossRefGoogle Scholar
  35. 35.
    Blackwell, J.A. Jr.: Numerical method to calculate the induced drag or optimum loading for arbitrary non-planar aircraft (1976)Google Scholar
  36. 36.
    Falkner, V.: The solution of lifting-plane problems by vortex-lattice theory. Ministry of Supply, Aeronautical Research Council (1947)Google Scholar
  37. 37.
    Prandtl, L.: Tragflügeltheorie. I. Mitteilung. Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen. Mathematisch-Physikalische Klasse 1918, 451–477 (1918)Google Scholar
  38. 38.
    Finck, R., Hoak, D.: USAF stability and control DATCOM. Engineering Documents (1978)Google Scholar
  39. 39.
    Lyu, X., Gu, H., Wang, Y., Li, Z., Shen, S., Zhang, F.: Design and implementation of a quadrotor tail-sitter vtol UAV. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3924–3930. IEEE (2017)Google Scholar
  40. 40.
    Zhang, F., Lyu, X., Wang, Y., Gu, H., Li, Z.: Modeling and flight control simulation of a quadrotor tailsitter vtol UAV. In: AIAA Modeling and Simulation Technologies Conference, p. 1561 (2017)Google Scholar
  41. 41.
    Brandt, J., Selig, M.: Propeller performance data at low Reynolds numbers. In: 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, p. 1255 (2011)Google Scholar
  42. 42.
    John, B.B., Robert, W.D., Gavin, K.A., Michael, S.S.: Apc propeller. http://m-selig.ae.illinois.edu/props/prop{DB}.html (1999)
  43. 43.
    Traub, L.: Validation of endurance estimates for battery powered UAVs. Aeronaut. J. 117(1197), 1155–1166 (2013)CrossRefGoogle Scholar
  44. 44.
    Kannan, R., Monma, C.L.: On the computational complexity of integer programming problems. In: Optimization and Operations Research, pp. 161–172. Springer (1978)Google Scholar
  45. 45.
    Powell, M.J.: A fast algorithm for nonlinearly constrained optimization calculations. In: Numerical Analysis, pp. 144–157. Springer (1978)Google Scholar
  46. 46.
    Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Math. Program. 89(1), 149–185 (2000)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Wright, S.J.: Coordinate descent algorithms. Math. Program. 151(1), 3–34 (2015)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Grippo, L., Sciandrone, M.: On the convergence of the block nonlinear Gauss–Seidel method under convex constraints. Oper. Res. Lett. 26(3), 127–136 (2000)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Saeed, A.S., Younes, A.B., Islam, S., Dias, J., Seneviratne, L., Cai, G.: A review on the platform design, dynamic modeling and control of hybrid UAVs. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 806–815. IEEE (2015)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Electronic & Computer EngineeringThe Hong Kong University of Science and TechnologyKowloonHong Kong
  2. 2.Mechanical EngineeringUniversity of Hong KongPokfulamHong Kong

Personalised recommendations