Journal of Intelligent & Robotic Systems

, Volume 96, Issue 3–4, pp 555–572 | Cite as

Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter

  • Yujiang Zhong
  • Wei Zhang
  • Youmin ZhangEmail author
  • Junyi Zuo
  • Hao Zhan


This paper proposes a new nonlinear fault detection and diagnosis (FDD) scheme for the inertial measurement unit (IMU) sensor of an unmanned quadrotor helicopter (UQH). To mitigate the impact of model uncertainties, the kinematic model of an UQH rather than the dynamic model is employed to design the FDD scheme. A two-stage extended Kalman filter (TSEKF) is developed for detecting, isolating and identifying IMU sensor faults. Considering that the TSEKF is insensitive to time-varying faults, two adaptive two-stage extended Kalman filters are further proposed by integrating TSEKF with different forgetting factor schemes. Several experiments have been designed and implemented on an UQH platform to test the proposed FDD scheme, where bias fault, drift fault and oscillatory fault are considered. The results demonstrate that the proposed FDD methods are effective for detecting and estimating the IMU sensor faults in different fault scenarios.


Unmanned quadrotor helicopter Fault diagnosis IMU sensor fault Adaptive two-stage extended Kalman filter 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors would like to thank the financial support received from the National Natural Science Foundation of China (No. 61473227, 11472222, 61573282 and 61833013) and Natural Sciences and Engineering Research Council of Canada. Authors would also like to thank the editor and the reviewers for the valuable comments and suggestions to improve the quality of the paper through review process.


  1. 1.
    Abbaspour, A., Aboutalebi, P., Yen, K.K., Sargolzaei, A.: Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV. ISA Trans. 67, 317–329 (2017)CrossRefGoogle Scholar
  2. 2.
    Aboutalebi, P., Abbaspour, A., Forouzannezhad, P., Sargolzaei, A.: A novel sensor fault detection in an unmanned quadrotor based on adaptive neural observer. J. Intell. Robot. Syst. 90(3-4), 473–484 (2018)CrossRefGoogle Scholar
  3. 3.
    Al Younes, Y., Rabhi, A., Noura, H., El Hajjaji, A.: Sensor Fault Diagnosis and Fault Tolerant Control Using Intelligent-Output-Estimator Applied on Quadrotor UAV. In: Proceeding of the international conference on unmanned aircraft systems (ICUAS), pp. 1117–1123 (2016)Google Scholar
  4. 4.
    Alexis, K., Nikolakopoulos, G., Tzes, A.: Model predictive quadrotor control: attitude, altitude and position experimental studies. IET Contr. Theory Appl. 6(12), 1812–1827 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Avram, R., Zhang, X., Muse, J.: Quadrotor accelerometer and gyroscope sensor fault diagnosis with experimental results. In: Annual conference of the prognostics and health management society, vol. 8, pp 625–623 (2015)Google Scholar
  6. 6.
    Avram, R.C., Zhang, X.D., Campbell, J., Muse, J.: IMU Sensor fault diagnosis and estimation for quadrotor UAVs. IFAC-PapersOnLine 48(21), 380–385 (2015)CrossRefGoogle Scholar
  7. 7.
    Avram, R.C., Zhang, X.D., Muse, J.: Quadrotor sensor fault diagnosis with experimental results. J. Intell. Robot. Syst. 86(1), 115–137 (2017)CrossRefGoogle Scholar
  8. 8.
    Benini, A., Mancini, A., Longhi, S.: An IMU/UWB/ Vision-based extended Kalman filter for mini-UAV localization in indoor environment using 802.15.4a wireless sensor network. J. Intell. Robot. Syst. 70(1-4), 461–476 (2013)CrossRefGoogle Scholar
  9. 9.
    Bresciani, T.: Modelling, identification and control of a quadrotor helicopter. MSc Thesis (2008)Google Scholar
  10. 10.
    Chang, J., Cieslak, J., Dávila, J., Zhou, J., Zolghadri, A., Guo, Z.: A two-step approach for an enhanced quadrotor attitude estimation via IMU data. IEEE Trans. Control Syst. Technol. 99, 1–9 (2017)Google Scholar
  11. 11.
    Eva Wu, N., Zhang, Y.M., Zhou, K.M.: Detection, estimation, and accommodation of loss of control effectiveness. Int. J. Adapt. Control Signal Process. 14(7), 775–795 (2000)CrossRefGoogle Scholar
  12. 12.
    Freddi, A., Longhi, S., Monteriù, A.: A diagnostic Thau observer for a class of unmanned vehicles. J. Intell. Robot. Syst. 67(1), 61–73 (2012)CrossRefGoogle Scholar
  13. 13.
    Hajiyev, C., Soken, H.E.: Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults. Aerosp. Sci. Technol. 28(1), 376–383 (2013)CrossRefGoogle Scholar
  14. 14.
    Hsieh, C.S., Chen, F.C.: Optimal solution of the two-stage Kalman estimator. IEEE Trans. Autom. Control. 44(1), 194–199 (1999)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Keller, J.Y., Darouach, M.: Optimal two-stage Kalman filter in the presence of random bias. Automatica. 33(9), 1745–1748 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kendall, F., Winnefeld, J.: Unmanned systems integrated roadmap FY 2011-2036. Office of the Secretary of Defense (2011)Google Scholar
  17. 17.
    Leishman, R.C., Macdonald, J.C., Beard, R.W., McLain, T.W.: Quadrotors and accelerometers: State estimation with an improved dynamic model. IEEE Control Syst. 34(1), 28–41 (2014)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Lu, P.: Fault diagnosis and fault-tolerant control for aircraft subjected to sensor and actuator faults. Doctoral dissertation Delft University of Technology (2016)Google Scholar
  19. 19.
    Lu, P., Van Eykeren, L., Van Kampen, E., Chu, Q.P.: Selective-reinitialization multiple-model adaptive estimation for fault detection and diagnosis. J. Guid. Control Dyn. 38(8), 1409–1424 (2015)CrossRefGoogle Scholar
  20. 20.
    Lu, P., Van Eykeren, L., Van Kampen, E.J., 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, p. 1145 (2014)Google Scholar
  21. 21.
    Lu, P., Van Kampen, E., Yu, B.: Actuator Fault Detection and Diagnosis for Quadrotors. In: 2014 international micro air vehicle conference and competition (IMAV 2014), pp. 58–63 (2014)Google Scholar
  22. 22.
    Parkum, J.E., Poulsen, N.K., Holst, J.: Recursive forgetting algorithms. Int. J. Control. 55(1), 109–128 (1992)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Pourbabaee, B., Meskin, N., Khorasani, K.: Sensor fault detection and isolation using multiple robust filters for linear systems with time-varying parameter uncertainty and error variance constraints. In: IEEE conference on control applications (CCA), pp. 382–389 (2014)Google Scholar
  24. 24.
    Qin, L.G., He, X., Yan, R., Zhou, D.H.: Active fault-tolerant control for a quadrotor with sensor faults. J. Intell. Robot. Syst. 88(2-4), 449–467 (2017)CrossRefGoogle Scholar
  25. 25.
    Rafaralahy, H., Richard, E., Boutayeb, M., Zasadzinski, M.: Simultaneous observer based sensor diagnosis and speed estimation of unmanned aerial vehicle. In: 47Th IEEE conference on decision and control, pp. 2938–2943 (2008)Google Scholar
  26. 26.
    Wu, N.E., Zhang, Y.M., Zhou, K.M.: Control effectiveness estimation using an adaptive Kalman estimator. In: Proceeding of the intelligent control (ISIC), pp. 181–186 (1998)Google Scholar
  27. 27.
    Yoon, S., Kim, S., Bae, J., Kim, Y., Kim, E.: Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system. Control Eng. Pract. 19(2), 158–173 (2011)CrossRefGoogle Scholar
  28. 28.
    Zhang, Y.M., Chamseddine, A., Rabbath, C., Gordon, B., Su, C.Y., Rakheja, S., Fulford, C., Apkarian, J., Gosselin, P.: Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed. J. Frankl. Inst. 350(9), 2396–2422 (2013)CrossRefGoogle Scholar
  29. 29.
    Zhong, M.Y., Guo, J., Guo, D.F., Yang, Z.H.: An extended h i/h optimization approach to fault detection of INS/GPS-integrated system. IEEE Trans. Instrum. Meas. 65(11), 2495–2504 (2016)CrossRefGoogle Scholar
  30. 30.
    Zhong, Y.J., Zhang, W., Zhang, Y.M.: Sensor fault diagnosis for unmanned quadrotor helicopter via adaptive two-stage extended Kalman filter. In: International conference on sensing, diagnostics, prognostics and control (SDPC), pp. 493–498 (2017)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of AeronauticsNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Mechanical, Industrial, Aerospace EngineeringConcordia UniversityMontrealCanada

Personalised recommendations