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Hybrid Model Predictive Control for Crosswind Stabilization of Hybrid Airships

  • Julian F. M. FoersterEmail author
  • Mohamed K. Helwa
  • Xintong Du
  • Angela P. Schoellig
Conference paper
  • 398 Downloads
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 11)

Abstract

A hybrid airship is an aerial vehicle that generates lift by leveraging both buoyancy and aerodynamic principles. The operation of such a vehicle can be limited by its high susceptibility to crosswinds during taxiing, take-off and landing. With the goal to mitigate this issue, this paper proposes a novel controller design for a stabilization system consisting of wing tip thrusters. Due to the response of the vehicle to wind disturbances (e.g. lifting off a wheel during taxiing), modeling it as a hybrid dynamical system is appropriate. A novel, customized hybrid model predictive control (MPC) scheme is proposed for crosswind stabilization. As shown in simulation as well as in experimental results in controlled and realistic environments, the proposed control scheme succeeds in stabilizing the vehicle despite artificial or actual wind disturbances, even in scenarios where simple linear MPC fails. Simultaneously, our approach is computationally efficient enough to run on an onboard computer.

References

  1. 1.
    Khoury, G.A.: Airship Technology, 2nd edn. Cambridge University Press, Cambridge (2012)Google Scholar
  2. 2.
    Jiron, Z.B.: Hybrid airships for lift: a new lift paradigm and a pragmatic assessment of the vehicle’s key operational challenges. Technical report, Air University, Maxwell Air Force Base, Alabama, December 2011Google Scholar
  3. 3.
    Airplane Flying Handbook. U.S. Department of Transportation, Federal Aviation Administration, Flight Standards Service (2016)Google Scholar
  4. 4.
  5. 5.
    Waslander, S., Wang, C.: Wind disturbance estimation and rejection for quadrotor position control. In: AIAA Infotech@ Aerospace Conference, p. 1983 (2009)Google Scholar
  6. 6.
    Demitri, Y., Verling, S., Stastny, T., Melzer, A., Siegwart, R.: Model-based wind estimation for a hovering VTOL tailsitter UAV. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3945–3952 (2017)Google Scholar
  7. 7.
    Corona, D., De Schutter, B.: Adaptive cruise control for a SMART car: a comparison benchmark for MPC-PWA control methods. IEEE Trans. Control Syst. Technol. 16(2), 365–372 (2008)CrossRefGoogle Scholar
  8. 8.
    Oberdieck, R., Pistikopoulos, E.N.: Explicit hybrid model-predictive control: the exact solution. Automatica 58, 152–159 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Giorgetti, N., Ripaccioli, G., Bemporad, A., Kolmanovsky, I., Hrovat, D.: Hybrid model predictive control of direct injection stratified charge engines. IEEE/ASME Trans. Mechatron. 11(5), 499–506 (2006)CrossRefGoogle Scholar
  10. 10.
    Maitland, A., McPhee, J.: Fast NMPC with mixed-integer controls using quasi-translations. In: 6th IFAC Conference on Nonlinear Model Predictive Control (2018)Google Scholar
  11. 11.
    Engell, S., Frehse, G., Schnieder, E.: Modelling, Analysis and Design of Hybrid Systems, vol. 279. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  12. 12.
    Sontag, E.D.: Nonlinear regulation: the piecewise linear approach. IEEE Trans. Autom. Control 26(2), 346–358 (1981)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Helwa, M.K., Esser, A., Schoellig, A.P.: Estimation-based model predictive control for automatic crosswind stabilization of hybrid aerial vehicles. arXiv preprint arXiv:1810.00046 [cs.SY] (2018)
  14. 14.
    Soman, S.S., Zareipour, H., Malik, O., Mandal, P.: A review of wind power and wind speed forecasting methods with different time horizons. In: North American Power Symposium, pp. 1–8 (2010)Google Scholar
  15. 15.
    Camacho, E.F., Ramirez, D.R., Limon, D., Munoz de la Pena, D., Alamo, T.: Model predictive control techniques for hybrid systems. Ann. Rev. Control 34, 21–31 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Julian F. M. Foerster
    • 1
    Email author
  • Mohamed K. Helwa
    • 1
    • 2
  • Xintong Du
    • 1
  • Angela P. Schoellig
    • 1
  1. 1.Dynamic Systems LabUniversity of Toronto Institute for Aerospace StudiesTorontoCanada
  2. 2.Electrical Power and Machines DepartmentCairo UniversityGizaEgypt

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