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Nonlinear Quadrotor Control with Online Model Identification

  • Peng LuEmail author
  • Erik-Jan van Kampen
  • Qiping P. Chu

Abstract

This paper proposes a new Fault Tolerant Control (FTC) system for quadrotorswhich are subjected to actuator loss of effectiveness faults. The proposed FTC system is composed of three subsystems: the state estimation, the loss of effectiveness estimation and the Backstepping (BS) controller. A new method to estimate the loss of effectiveness online is proposed, which can provide fault information for the controller to achieve fault tolerant control. The performance of the FTC system is validated using two different simulations: position control of the quadrotor in the presence and absence of actuator faults. The simulation results show that the proposed system can enable the quadrotor to maintain the flight even all the four rotors fail consecutively, which demonstrate its satisfactory performance.

Keywords

Unman Aerial Vehicle Inertial Measurement Unit Angular Rate Actuator Fault Fault Tolerant Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Amoozgar, M.H., Chamseddine, A., Zhang, Y.: Experimental Test of a Two-Stage Kalman Filter for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter. Journal of Intelligent & Robotic Systems 70(1-4), 107–117 (2012)CrossRefGoogle Scholar
  2. 2.
    Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying. PhD thesis, Ecole Polytechnique Fedeale de lausanne (2007)Google Scholar
  3. 3.
    Bouabdallah, S., Siegwart, R.: Full control of a quadrotor. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, oct 2007, pp. 153–158. IEEE Computer Society Press, Los Alamitos (2007)CrossRefGoogle Scholar
  4. 4.
    Cen, Z., Noura, H., Susilo, T.B., Younes, Y.A.: Robust Fault Diagnosis for Quadrotor UAVs Using Adaptive Thau Observer. Journal of Intelligent & Robotic Systems 73(1-4), 573–588 (2013)CrossRefGoogle Scholar
  5. 5.
    Fay, G.: Derivation of the Aerodynamic Forces for the Mesicopter Simulation, pp. 1–8 (2001)Google Scholar
  6. 6.
    Freddi, A., Longhi, S., Monteriù, A.: A Diagnostic Thau Observer for a Class of Unmanned Vehicles. Journal of Intelligent & Robotic Systems 67(1), 61–73 (2012)CrossRefzbMATHGoogle Scholar
  7. 7.
    Krstic, M., Kanellakopoulos, I., Kokotovic, P.: Nonlinear and Adaptive Control Design. John Wiley & Sons, Inc. (1995)Google Scholar
  8. 8.
    Levant, A.: Robust exact differentiation via sliding mode technique. Automatica 34(3), 379–384 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Lombaerts, T.J.J., Chu, Q.P., Mulder, J.a., Joosten, D.a.: Modular flight control reconfiguration design and simulation. Control Engineering Practice 19(6), 540–554 (2011)CrossRefGoogle Scholar
  10. 10.
    Lu, P., Van Eykeren, L., Kampen, E.v., Chu, Q.P., Yu, B.: Adaptive Hybrid Unscented Kalman Filter for Aircraft Sensor Fault Detection, Isolation and Reconstruction. In: AIAA Guidance, Navigation, and Control Conference, National Harbor, Maryland, pp. 1–18 (2014)Google Scholar
  11. 11.
    Marconi, L., Naldi, R.: Robust full degree-of-freedom tracking control of a helicopter. Automatica 43(11), 1909–1920 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Mulder, J.A., Chu, Q.P., Sridhar, J.K., Breeman, J.H., Laban, M.: Non-linear aircraft flight path reconstruction review and new advances. Progress in Aerospace Sciences 35, 673–726 (1999)CrossRefGoogle Scholar
  13. 13.
    Raffo, G.V., Ortega, M.G., Rubio, F.R.: An integral predictive/nonlinear control structure for a quadrotor helicopter. Automatica 46(1), 29–39 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Slegers, N., Kyle, J., Costello, M.: Nonlinear Model Predictive Control Technique for Unmanned Air Vehicles. Journal of Guidance, Control, and Dynamics 29(5), 1179–1188 (2006)CrossRefGoogle Scholar
  15. 15.
    Van Eykerenand, L., Chu, Q.P.: Air Data Sensor Fault Detection using Kinematic Relations. In: Proceedings of the EuroGNC 2013, 2nd CEAS Special Conference on Guidance, Navigation & Control, pp. 414–428 (2013)Google Scholar
  16. 16.
    Zhang, X., Zhang, Y., Su, C.-y., Feng, Y.: Fault Tolerant Control for Quadrotor via Backstepping Approach. In: AIAA Aerospace Sciences Meeting, Orlandeo, Florida, pp. 1–12 (2010)Google Scholar
  17. 17.
    Zhang, Y., Chamseddine, A.: Fault Tolerant Flight Control Techniques with Application to a Quadrotor UAV Testbed (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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