Journal of Intelligent & Robotic Systems

, Volume 80, Issue 1, pp 139–164 | Cite as

Development and Modeling of a Low-Cost Unmanned Aerial Vehicle Research Platform

  • Ony Arifianto
  • Mazen FarhoodEmail author


This paper describes the development and modeling of a low-cost and reliable small unmanned aerial vehicle research platform for advanced control implementation. The platform is mostly constructed of low-cost commercial-off-the-shelf (COTS) components. The only non-COTS components are the airdata probes, which are manufactured and calibrated in-house. The airframe used is the commercially available radio-controlled (R/C) 6-foot Telemaster airplane from Hobby Express, chosen mainly for its adequately spacious fuselage and for being reasonably stable and sufficiently agile. One noteworthy feature of this platform is the use of two separate low-cost onboard computers for handling the data management/hardware interfacing and control computation. Specifically, the single board computer, Gumstix Overo Fire, is used to execute the control algorithms, whereas the open source autopilot, Ardupilot Mega, is mostly used to interface the Overo computer with the sensors and actuators. The platform supports multi-vehicle operations through the use of a radio modem that enables multi-point communications. As the goal of this platform is to implement rigorous control algorithms for real-time trajectory tracking and distributed control, it is important to derive an appropriate flight dynamic model of the platform, based on which the controllers will be synthesized. For that matter, the paper provides reasonably accurate models of the vehicle, servomotors, and propulsion system. Namely, the output error method is used to estimate the longitudinal and lateral-directional aerodynamic parameters from flight test data. The moments of inertia of the platform are determined using the simple pendulum test method, and the frequency response of each servomotor is also obtained experimentally. The Javaprop applet is used to obtain lookup tables relating airspeed to propeller thrust at constant throttle settings.


Unmanned aerial vehicle Small fixed-wing aircraft Five-hole probe Flight dynamic model Time-domain system identification 


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  1. 1.
    Ardupilot Mega. Accessed 23 July 2014 (2014)
  2. 2.
    USA Standard Atmosphere. USA Government Printing Office, Washington (1976)Google Scholar
  3. 3.
    Arifianto, O.: A low-cost unmanned aerial vehicle research platform: Development, modeling, and advanced control implementation. Ph.D. thesis, Department of Aerospace and Ocean Engineering, Virginia Tech (2013)Google Scholar
  4. 4.
    Arifianto, O., Farhood, M.: Optimal control of fixed-wing uavs along real-time trajectories. In: 5th Annual DSCC and 11th MOVIC (2012)Google Scholar
  5. 5.
    Barlow, J.B., Rae, W.H., Pope, A., 3rd ed.: Low-speed wind tunnel testing. Wiley-Interscience (1999)Google Scholar
  6. 6.
    Bryer, D.W., Walshe, D.E.: Pressure probes selected for three-dimensional flow measurement. Reports and memoranda 3037, national advisory committee for aeronautics (1955)Google Scholar
  7. 7.
    Cabecinhas, D., Silvestre, C., Rosa, P., Cunha, R.: Path-following control for coordinated turn aircraft maneuvers. In: AIAA Guidance, Navigation and Control Conference and Exhibit (2007)Google Scholar
  8. 8.
    Craig, J.J., 2nd ed.: Introduction to robotics: mechanics and control. Addison-Wesley Longman Publishing Co., Inc., MA, USA (1989)zbMATHGoogle Scholar
  9. 9.
    Dansker, O.D., Johnson, M.J., Selig, M.S., Bretl, T.W.: Development of the uiuc aero testbed: a large-scale unmanned electric aerobatic aircraft for aerodynamics research. In: (AIAA) Applied Aerodynamics Conference (2013)Google Scholar
  10. 10.
    Dominy, R.G., Hodson, H.P.: An investigation of factors influencing the calibration of five-hole-probe for three-dimensional flow measurements. J. Turbomach. 115, 513–519 (1993)CrossRefGoogle Scholar
  11. 11.
    Dorobantu, A., Murch, A., Mettler, B., Balas, G.: System identification for small, low-cost, fixed-wing unmanned aircraft. J. Aircr. 50(4), 1117–1130 (2013)CrossRefGoogle Scholar
  12. 12.
    Farhood, M.: Nonstationary LPV control for trajectory tracking: a double pendulum example. Int. J. Control. 85(5), 545–562 (2012)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Farhood, M., Di, Z., Dullerud, G.E.: Distributed control of linear time-varying systems interconnected over arbitrary graphs. International Journal of Robust and Nonlinear Control. doi: 10.1002/rnc.3081
  14. 14.
    Farhood, M., Dullerud, G.E.: Control of nonstationary LPV systems. Automatica 44(8), 2108–2119 (2008)zbMATHMathSciNetCrossRefGoogle Scholar
  15. 15.
    Farhood, M., Dullerud, G.E.: Control of systems with uncertain initial conditions. IEEE Trans. Autom. Control 53(11), 2646–2651 (2008)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fisher, R.A.: On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 222, pp. 309368. Accessed 23 July 2014 (1922)
  17. 17.
    Gibbs, B.P.: Advanced Kalman filtering, least-squares and modeling: A practical handbook. Wiley, Hoboken, NJ (2011)CrossRefGoogle Scholar
  18. 18.
    Gracey, W.: Summary of methods of measuring angle of attack. Technical note 4351, national advisory committee for aeronautics (1958)Google Scholar
  19. 19.
    Green, M.W.: Measurement of the moments of inertia of full scale airplanes. NACA technical note (1927)Google Scholar
  20. 20.
    Hartley, R.F., Hugon, F.: Development and flight testing of a model based autopilot library for a low cost unmanned aerial systems. In: (AIAA) Guidance, navigation and control conference (2013)Google Scholar
  21. 21.
    Hepperle, M.: Javaprop - design and analysis of propellers.
  22. 22.
    J. E. Zeis, C.U.: Angle of attack and sideslip estimation using inertial reference platform. Master’s thesis, Air Force Institute of Technology (1988)Google Scholar
  23. 23.
    Jardin, M.R., Mueller, E.R.: Optimized measurements of unmanned-air-vehicle mass moment of inertia with a bifilar pendulum. J. Aircr. 46, 63–75 (2009)CrossRefGoogle Scholar
  24. 24.
    Jategaonkar, R.V.: Flight Vehicle System Identification: a time domain methodology. Progress in astronautics and aeronautics. American Institute of Aeronautics and Astronautics (2006)Google Scholar
  25. 25.
    Jordan, T., Foster, J., Bailey, R., Belcastro, C.: Airstar: a uav platform for flight dynamics and control system testing. In: (AIAA) 25th Aerodynamic and Measurement Technology and Ground Testing Conference (2006)Google Scholar
  26. 26.
    Jung, D., Levy, E.J., Zhou, D., Fink, R., Moshe, J., Earl, A., Tsiotras, P.: Design and development of a low-cost test-bed for undergraduate education in UAVs. In: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, pp. 27392744. Seville, Spain (2005)Google Scholar
  27. 27.
    Kaminer, I., Pascoal, A., Hallberg, E., Silvestre, C.: Trajectory tracking for autonomous vehicles: An integrated approach to guidance and control. J. Guid. Control. Dyn. 21(1), 29–38 (1998)zbMATHCrossRefGoogle Scholar
  28. 28.
    Kane, T.R., tai Tseng, G.: Dynamics of the bifilar pendulum. Int. J. Mech. Sci. 9, 83–96 (1967)zbMATHCrossRefGoogle Scholar
  29. 29.
    Klein, V., Morelli, E.A.: Aircraft system identification: theory and practice. American Institute of Aeronautics and Astronautics (2006)Google Scholar
  30. 30.
    Manaï, M., Desbiens, A., Gagnon, E.: Identification of a UAV and design of a hardware-in-the-loop system for nonlinear control purposes. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (2005)Google Scholar
  31. 31.
    Drela, M.: AVL. Accessed 23 July 2014
  32. 32.
    McLain, T.W., Beard, R.W.: Unmanned air vehicle testbed for cooperative control experiments. In: Proceedings of the American Control Conference, vol. 6, pp. 53275331. Boston, MA (2004)Google Scholar
  33. 33.
    Miller, M.P.: An accurate method of measuring the moments of inertia of airplanes. NACA Technical Note (1930)Google Scholar
  34. 34.
    Morelli, E.: Real-time aerodynamic parameter estimation without air flow angle measurements. J. Aircr. 49(4), 1064–1074 (2012)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Morrison, G.L., Schobeiri, M.T., Pappu, K.R.: Five-hole pressure probe analysis technique. Flow Meas. Instrum. 9, 153–158 (1998)CrossRefGoogle Scholar
  36. 36.
    Motter, M.A., Logan, M.J., French, M.L., Guerreiro, N.M.: Simulation to flight test for a UAV controls testbed. In: Proceedings of the 25th AIAA Aerodynamic Measurement Technology and Ground Testing Conference. San Francisco, CA (2006)Google Scholar
  37. 37.
    Mulder, J.A., Chu, Q.P., Sridhar, J.K., Breeman, J.H., Laban, M.: Non-linear aircraft flight path reconstruction review and new advances. Prog. Aerosp. Sci. 35, 673–726 (1999)CrossRefGoogle Scholar
  38. 38.
    Murch, A.M., Paw, Y.C., Pandita, R., Li, Z., Balas, G.J.: A low cost small UAV flight research facility. In: Holzapfel, F., Theil, S. (eds.) Advances in Aerospace Guidance, Navigation and Control, pp 29–40. Springer, Berlin Heidelberg (2011)CrossRefGoogle Scholar
  39. 39.
    Naughton, J.W., III, L.N.C., Settles, G.S.: A miniature, fast-response 5-hole probe for supersonic flowfield measurements. In: (AIAA) 30th Aerospace Sciences Meeting & Exhibit (1992)Google Scholar
  40. 40.
    Owens, D.B., Cox, D.E., Morelli, E.A.: Development of a low-cost sub-scale aircraft for flight research: the faser project. In: (AIAA) Aerodynamic Measurement Technology and Ground Testing Conference (2006)Google Scholar
  41. 41.
    Paul, A.R., Upadhyay, R.R., Jain, A.: A novel calibration algorithm for five-hole pressure probe. Int. J. Eng. Sci. Tech. 3, 88–95 (2011)CrossRefGoogle Scholar
  42. 42.
    Pereira, E., Hedrick, K., Sengupta, R.: The C3UV testbed for collaborative control and information acquisition using UAVs. In: Proceedings of the American Control Conference, pp. 14661471. Washington, DC (2013)Google Scholar
  43. 43.
    Raol, J., Singh, J.: Flight mechanics modeling snd analysis. CRC Press (2009)Google Scholar
  44. 44.
    Salman, S.A., Sreenatha, A.G., Choi, J.Y.: Attitude dynamics identification of unmanned aerial vehicle. Int J Control. Autom. Syst. 4(6), 782–787 (2006)Google Scholar
  45. 45.
    Soule, H.A., Miller, M.P.: Experimental determination of the moments of inertia of airplanes. NACA Technical Report (1933)Google Scholar
  46. 46.
    Telionis, D., Yang, Y., Rediniotis, O.: Recent development in multi-hole probe (mhp) technology. In: 20th International Congress of Mechanical Engineering (2009)Google Scholar
  47. 47.
    Tischler, M.B., Remple, R.K.: Aircraft and rotorcraft system identification: engineering methods with flight test examples. Am. Inst. Aeronaut. Astronaut. (2006)Google Scholar
  48. 48.
    Tomas, M.: Tornado, Accessed 23 July 2014
  49. 49.
    V.Hoffer, N., Coopmans, C., Jensen, A.M., Chen, Y.: A survey and categorization of small low-cost unmanned aerial vehicle system identification. J. Intell. & Robot. Syst. 74(1–2), 129–145 (2014)CrossRefGoogle Scholar
  50. 50.
    Walther, B.A., Moore, J.L.: The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28(6), 815–829 (2005)CrossRefGoogle Scholar
  51. 51.
    Williams, J.E., Vukelich, S.R.: The USAF stability and control digital DATCOM volume 1 users manual. DTIC-MIL (1979)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Aerospace and Ocean Engineering, Virginia TechBlacksburgUSA

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