Feather-Inspired Sensor for Stabilizing Unmanned Aerial Vehicles in Turbulent Conditions

  • Christos KouppasEmail author
  • Martin Pearson
  • Paul Dean
  • Sean Anderson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10384)


Stabilizing unmanned aerial vehicles (UAVs) in turbulent conditions is a challenging problem. Typical methods of stabilization do not use feedforward information about the airflow disturbances but only UAV attitude feedback signals, e.g. from an inertial measurement unit. The novel proposal of this work is the development of a feather-inspired sensor and feedforward controller that transforms from sensed turbulent airflow to feedforward control action for improving the stability of the UAV. The feedforward controller was based on fuzzy logic, combined in a feedforward-feedback loop with a standard PID control system. An experimental rig based on a one degree of freedom helicopter plant (elevation only) was developed to evaluate the potential of the sensor and control algorithm. Evaluation results showed reduction of disturbance using the fuzzy feedforward-feedback scheme, under turbulent airflow, versus a classical feedback PID-controlled system.


UAV Feather-inspired sensor Turbulence sensor Turbulence reduction Feedback/feedforward fuzzy controller 


  1. 1.
    Blower, C.J., Lee, W., Wickenheiser, A.M.: The development of a closed-loop flight controller with panel method integration for gust alleviation using biomimetic feathers on aircraft wings. In: Proceedings of SPIE 8339, Bioinspiration, Biomimetics, and Bioreplication, No. 833901 (2012)Google Scholar
  2. 2.
    Brown, R.E., Fedde, M.R.: Airflow sensors in the avian wing. J. Exp. Biol. 179, 13–30 (1993)Google Scholar
  3. 3.
    Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460–466 (2015)CrossRefGoogle Scholar
  4. 4.
    Isermann, R.: On fuzzy logic applications for automatic control, supervision, and fault diagnosis. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 28(2), 221–235 (1998)CrossRefGoogle Scholar
  5. 5.
    Joyo, M.K., Hazry, D., Faiz Ahmed, S., Tanveer, M.H., Warsi, F.A., Hussain, A.T.: Altitude and horizontal motion control of quadrotor UAV in the presence of air turbulence. In: Proceedings of the IEEE Conference on Systems. Process & Control (ICSPC), pp. 16–20. IEEE, Kuala Lumpur, December 2013Google Scholar
  6. 6.
    Kouppas, C.: Feather-like sensor for stabilizing unmanned aerial vehicles in turbulent conditions. Master’s thesis, The University of Sheffield, Sheffield, UK (2016)Google Scholar
  7. 7.
    Kumar, V., Michael, N.: Opportunities and challenges with autonomous micro aerial vehicles. Int. J. Rob. Res. 31(11), 1279–1291 (2012)CrossRefGoogle Scholar
  8. 8.
    Lin, C.L., Hsiao, Y.H.: Adaptive feedforward control for disturbance torque rejection in seeker stabilizing loop. IEEE Trans. Control Syst. Technol. 9(1), 108–121 (2001)CrossRefGoogle Scholar
  9. 9.
    Mohamed, A., Abdulrahim, M., Watkins, S., Clothier, R.: Development and flight testing of a turbulence mitigation system for micro air vehicles. J. Field Rob. 33, 639–660 (2016)CrossRefGoogle Scholar
  10. 10.
    Mohamed, A., Clothier, R., Watkins, S., Sabatini, R., Abdulrahim, M.: Fixed-wing MAV attitude stability in atmospheric turbulence, part 1: suitability of conventional sensors. Prog. Aerosp. Sci. 70, 69–82 (2014)CrossRefGoogle Scholar
  11. 11.
    Mohamed, A., Watkins, S., Clothier, R., Abdulrahim, M., Massey, K., Sabatini, R.: Fixed-wing MAV attitude stability in atmospheric turbulence - part 2: investigating biologically-inspired sensors. Prog. Aerosp. Sci. 71, 1–13 (2014)CrossRefGoogle Scholar
  12. 12.
    Passino, K.M., Yurkovich, S.: Fuzzy Control. Addison Wesley Longman Inc., Menlo Park (1998)zbMATHGoogle Scholar
  13. 13.
    Rooney, T., Pearson, M.J., Pipe, T.: Measuring the local viscosity and velocity of fluids using a biomimetic tactile whisker. In: Wilson, S.P., Verschure, P.F.M.J., Mura, A., Prescott, T.J. (eds.) Living Machines 2015. LNCS, vol. 9222, pp. 75–85. Springer, Cham (2015). doi: 10.1007/978-3-319-22979-9_7 CrossRefGoogle Scholar
  14. 14.
    Shen, H., Xu, Y., Dickinson, B.: Fault tolerant attitude control for small unmanned aircraft systems equipped with an airflow sensor array. Bioinspiration Biomim. 9(4), 046015 (2014)CrossRefGoogle Scholar
  15. 15.
    Sterbing-D’Angelo, S., Chadha, M., Chiu, C., Falk, B., Xian, W., Barcelo, J., Zook, J.M., Moss, C.F.: Bat wing sensors support flight control. Proc. Nat. Acad. Sci. 108(27), 11291–11296 (2011)CrossRefGoogle Scholar
  16. 16.
    Sullivan, J.C., Mitchinson, B., Pearson, M.J., Evans, M., Lepora, N.F., Fox, C.W., Melhuish, C., Prescott, T.J.: Tactile discrimination using active whisker sensors. IEEE Sens. J. 12(2), 350–362 (2012)CrossRefGoogle Scholar
  17. 17.
    Taylor, G.K., Krapp, H.G.: Sensory systems and flight stability: what do insects measure and why? Adv. Insect Physiol. 34, 231–316 (2007)CrossRefGoogle Scholar
  18. 18.
    Xu, Y., Luo, D., Xian, N., Duan, H.: Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter. Optik 125(13), 3102–3106 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christos Kouppas
    • 1
    Email author
  • Martin Pearson
    • 2
  • Paul Dean
    • 3
  • Sean Anderson
    • 3
  1. 1.Loughborough UniversityLoughboroughUK
  2. 2.University of the West EnglandBristolUK
  3. 3.University of SheffieldSheffieldUK

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