Journal of Intelligent & Robotic Systems

, Volume 86, Issue 2, pp 255–276 | Cite as

Autolanding a Power-off UAV Using On-line Optimization and Slip Maneuvers



This paper tackles the final stages of autolanding a fixed-wing Unmanned Air Vehicle (UAV) in a power-fail emergency scenario. We applied the principle of Nonlinear Model Predictive Control (NMPC) to plan trajectories that respect the aircraft’s limits, and react on disturbances and environmental changes in real-time. However, thanks to judicious problem formulations, our algorithm optimizes—in each time step—the whole horizon of the landing trajectory, and settles a dynamic compromise between the different touchdown requirements. Furthermore, the algorithm is powered by a novel method that utilizes slip maneuvers to achieve adequate energy control notwithstanding the absence of specialized drag control devices. Thus, precise touchdown point is attained even in the presence of strong wind shear and large initial errors. All design parameters are optimized off-line over a wide spectrum of conditions using Genetic Algorithm (GA). Monte-Carlo simulation of the optimized design shows excellent landing performance and high success rate on both flat and sloping terrains.


Unmanned air vehicles Autolanding Emergency landing Nonlinear model predictive control Trajectory optimization Slip maneuvers Genetic algorithm 


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  1. 1.
    Fitzgerald, D., Walker, R.: Duncan Campbell. A vision based forced landing site selection system for an autonomous UAV. In: Intelligent Sensors, Proceedings of the International Conference on Sensor Networks and Information Processing Conference, pp 397–402. IEEE (2005)Google Scholar
  2. 2.
    Fitzgerald, D.L.: Landing site selection for UAV forced landings using machine vision (2007)Google Scholar
  3. 3.
    Warren, M., Mejias, L., Yang, X., Arain, B., Gonzalez, F., Upcroft, B.: Enabling aircraft emergency landings using active visual site detection. In: Field and Service Robotics, pp 167–181. Springer (2015)Google Scholar
  4. 4.
    Shen, Y.-F., Rahman, Z., Krusienski, D., Li, J.: A vision-based automatic safe landing-site detection system. IEEE Trans. Aerosp. Electron. Syst. 49(1), 294–311 (2013)CrossRefGoogle Scholar
  5. 5.
    Aiying, L., Ding, W., Li, H.: Multi-information based safe area step selection algorithm for UAV’s emergency forced landing. J. Softw. 8(4), 995–1002 (2013)Google Scholar
  6. 6.
    Dehshibi, M.M., Fahimi, M.S., Mashhadi, M.: Vision-based site selection for emergency landing of UAVs. In: Recent Advances in Information and Communication Technology 2015, pp 133–142. Springer (2015)Google Scholar
  7. 7.
    Coombes, M., Chen, W.-H., Render, P.: Reachability analysis of landing sites for forced landing of a UAS. J. Intell. Robot. Syst. 73(1-4), 635–653 (2014)CrossRefGoogle Scholar
  8. 8.
    Coombes, M., Chen, W.-H., Render, P.: Reachability analysis of landing sites for forced landing of a UAS in wind using trochoidal turn paths. In: International Conference on Unmanned Aircraft Systems (2015)Google Scholar
  9. 9.
    Eng, P., Mejias, L., Liu, X., Walker, R.: Automating human thought processes for a UAV forced landing. In: Selected papers from the 2nd International Symposium on UAVs, Reno, Nevada, USA June 8–10, 2009, pp 329–349. Springer (2010)Google Scholar
  10. 10.
    Atkins, E.M, Portillo, I.A., Strube, M.J: Emergency flight planning applied to total loss of thrust. J. Aircr. 43(4), 1205–1216 (2006)CrossRefGoogle Scholar
  11. 11.
    Dubins, L.E.: On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. Am. J. Math., 497–516 (1957)Google Scholar
  12. 12.
    Eng, P.: Path planning guidance and control for a UAV forced landing (2011)Google Scholar
  13. 13.
    Siegel, D.: Development of an autoland system for general aviation aircraft. PhD thesism, Massachusetts Institute of Technology Cambridge MA 02139 USA (2011)Google Scholar
  14. 14.
    Meng, S., Xiang, J., Luo, Z., Ren, Y., Zhuang, N.: A novel trajectory planning strategy for aircraft emergency landing using Gauss pseudospectral method. Control Theory and Technology 12(4), 393–401 (2014)CrossRefGoogle Scholar
  15. 15.
    Peng, T., Shuguang, Z., Lei, J., Zhi, T.: A novel emergency flight path planning strategy for civil airplanes in total loss of thrust. Procedia Engineering 17, 226–235 (2011)CrossRefGoogle Scholar
  16. 16.
    Hongying, W.: Felix Mora-Camino. Knowledge-based trajectory control for engine-out aircraft. In: Digital Avionics Systems Conference (DASC) IEEE/AIAA 32nd, pp 2B1–1. IEEE (2013)Google Scholar
  17. 17.
    Edwards, F.G., Foster, J.D.: Flight test results from the CV990 simulated space shuttle during unpowered automatic approaches and landings. NASA TM X-62, 285 (1973)Google Scholar
  18. 18.
    Zhi, L., Yong, W.: Intelligent landing of unmanned aerial vehicle using hierarchical fuzzy control. In: Aerospace Conference IEEE, p 2012. IEEE (2012)Google Scholar
  19. 19.
    Federal Aviation Administration: Glider Flying Handbook United States Department of Transportation (2003)Google Scholar
  20. 20.
    Etkin, B.: Dynamics of atmospheric flight Courier Corporation (2012)Google Scholar
  21. 21.
    Grüne, L., Pannek, J.: Nonlinear model predictive control Springer (2011)Google Scholar
  22. 22.
    Findeisen, R., Allgöwer, F.: An introduction to nonlinear model predictive control. In: 21st Benelux Meeting on Systems and Control, vol. 11, pp 119–141 (2002)Google Scholar
  23. 23.
    Kang, Y., Hedrick, J.K.: Linear tracking for a fixed-wing UAV using nonlinear model predictive control. IEEE Trans. Control Syst. Technol. 17(5), 1202–1210 (2009)CrossRefGoogle Scholar
  24. 24.
    Joos, A., Müller, M.A, Baumgärtner, D., Fichter, W., Allgöwer, F.: Nonlinear predictive control based on time-domain simulation for automatic landing. In: AIAA. AIAA Guidance, Control, and Dynamics Conference (2011)Google Scholar
  25. 25.
    Betts, J.T.: Survey of numerical methods for trajectory optimization. J. Guid. Control. Dyn. 21(2), 193–207 (1998)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Betts, J.T.: Practical methods for optimal control and estimation using nonlinear programming, volume 19 Siam (2010)Google Scholar
  27. 27.
    Lambregts, A.A.: Avoiding the pitfalls in automatic landing control system design. AIAA Paper 82, 1599 (1982)Google Scholar
  28. 28.
    Hongying, W., Mora-Camino, F.: Glide control for engine-out aircraft. In: AIAA GNC Guidance, Navigation, and Control Conference, p 2012 (2012)Google Scholar
  29. 29.
    Cárdenas, E.M, Boschetti, P.J, Amerio, A., Velásquez, C.D.: Design of an unmanned aerial vehicle for ecological conservation. AIAA Paper, 7056 (2005)Google Scholar
  30. 30.
    Boschetti, A.A.P.J., Cárdenas, E.M., Arévalo, Á.: Stability and performance of a light unmanned airplane in ground effect. In: 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition (2010)Google Scholar
  31. 31.
    Boschetti, P.J., Cárdenas, E.M.: Ground effect on the longitudinal stability of an unmanned airplane. In: 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, p 1051 (2012)Google Scholar
  32. 32.
    Johnson, S.G.: The NLopt nonlinear-optimization package.

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Mohammed Al Masri
    • 1
  • Samer Dbeis
    • 1
  • Michel Al Saba
    • 1
  1. 1.Higher Institute for Applied Sciences and Technology (HIAST)DamascusSyria

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