Innovations in Systems and Software Engineering

, Volume 1, Issue 2, pp 157–175 | Cite as

Online safety calculations for glide-slope recapture

  • Jonathan Sprinkle
  • Aaron D. Ames
  • J. Mikael Eklund
  • Ian M. Mitchell
  • S. Shankar Sastry


As unmanned aerial vehicles (UAVs) increase in popularity and usage, an appropriate increase in confidence in their behavior is expected. This research addresses a particular portion of the flight of an aircraft (whether autonomous, unmanned, or manned): specifically, the recapture of the glide slope after a wave-off maneuver during landing. While this situation is rare in commercial aircraft, its applicability toward unmanned aircraft has been limited due to the complexity of the calculations of safety of the maneuvers. In this paper, we present several control laws for this glide-slope recapture, and inferences into their convergence to the glide slope, as well as reachability calculations which show their guaranteed safety. We also present a methodology which theoretically allows us to apply these offline-computed safety data to all kinds of unmanned fixed-wing aerial vehicles while online, permitting the use of the controllers to reduce wait times during landing. Finally, we detail the live aircraft application demonstration which was done to show feasibility of the controller, and give the results of offline simulations which show the correctness of online decisions at that demonstration.


Unmanned aerial vehicles (UAVs) Reachability model analysis Controller synthesis Code generation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adams MJ, Tenney YJ, Pew RW (1995) Situation awareness and the cognitive management of complex systems. Hum Factors 37(1):85–104Google Scholar
  2. 2.
    Bayen A, Tomlin C, Ye Y, Zheng J (2004) An approximation algorithm for scheduling aircraft with holding time. In: Proceedings of the 43rd IEEE conference on decision and control, vol 3, pp. 2760–2767Google Scholar
  3. 3.
    Callander BD (1998) The evolution of air mobility. J Air Force Assoc 81(2)Google Scholar
  4. 4.
    Cardaliaguet P, Quincampoix M, Saint-Pierre P (1999) Set-valued numerical analysis for optimal control and differential games. In: Bardi M, Raghavan TES, Parthasarathy T (eds.) Stochastic and differential games: theory and numerical methods, annals of international society of dynamic games, vol 4, Birkhäuser, pp. 177–247Google Scholar
  5. 5.
    Eklund JM, Sprinkle J, Sastry SS (2005) Template based planning and distributed control for networks of unmanned underwater vehicles. In: 44th IEEE conference on decision and control and European control conference ECC 2005 (CDC-ECC’05), (submitted for publication)Google Scholar
  6. 6.
    Endsley MR, Strauch B (1997) Automation and situation awareness: The accident at Cali, Columbia. In: Jensen RS, Rakovan L (eds) Proceedings of the ninth international symposium on aviation psychology, pp. 877–881Google Scholar
  7. 7.
    Greenstreet MR (1996) Verifying safety properties of differential equations. In: Proceedings of the 1996 conference on computer aided verification, New Brunswick, NJ, pp. 277–287Google Scholar
  8. 8.
  9. 9.
    Koo TJ, Sastry SS (2003) Hybrid control of unmanned aerial vehicles for autonomous landing. In: Proceedings of 2nd AIAA ‘‘Unmanned Unlimited’’, AIAA, systems, technologies, and operations-aerospace, land, and sea conferenceGoogle Scholar
  10. 10.
    Meingast M, Geyer C, Sastry SS (2004) Vision based terrain recovery for landing unmanned aerial vehicles. In: 43rd IEEE conference on decision and control, vol. 2, pp. 1670–1675Google Scholar
  11. 11.
    Mitchell I, Tomlin CJ (2003) Overapproximating reachable sets by Hamilton-Jacobi projections. J Sci Comput 19(1–3):323–346Google Scholar
  12. 12.
    Mitchell I, Bayen A, Tomlin CJ (2005) A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games, IEEE Trans Automat Cont (to appear)Google Scholar
  13. 13.
    Osher S, Fedkiw R (2002) Level set methods and dynamic implicit surfaces. Springer, Berlin Heidelberg New YorkGoogle Scholar
  14. 14.
    Sprinkle J, Eklund JM, Kim HJ, Sastry SS (2004) Encoding aerial pursuit/evasion games with fixed wing aircraft into a nonlinear model predictive tracking controller. In: Proceedings of the 43rd IEEE conference on decision and control, vol 3, pp. 2609–2614Google Scholar
  15. 15.
    Sprinkle J, Eklund JM, Sastry SS (2005) Deciding to land a UAV safely in real time. In: Proceedings of American Control Conference (ACC) 2005 (In Publication)Google Scholar
  16. 16.
    Stevens BL, Lewis FL (2003) Aircraft control and simulation, 2nd edn. Wiley-IEEE, ISBN 0471371459Google Scholar
  17. 17.
    Tomlin C, Mitchell I, Bayen A, Oishi M (2003) Computational techniques for the verification of hybrid systems. Proc IEEE 91(7):986–1001Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jonathan Sprinkle
    • 1
  • Aaron D. Ames
    • 1
  • J. Mikael Eklund
    • 1
  • Ian M. Mitchell
    • 2
  • S. Shankar Sastry
    • 1
  1. 1.Dept. of Electrical Engineering & Computer Sciences 231 Cory HallUniversity of CaliforniaBerkeley BerkeleyUSA
  2. 2.Computer Science Department ICICS/CS 217University of British ColumbiaVancouverCanada

Personalised recommendations