Skip to main content
Log in

Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In order to improve the reliability of robotic systems, various fault detection and isolation (FDI) algorithms have been proposed. However, most of these algorithms are model-based and thus, an accurate model of the robot is required although it is hard to obtain and often time-varying. Acceleration estimation is an additional challenge in dynamic model-based algorithms as it is hard to measure accurately in practice. In this study, a neural network based fault detection algorithm that does not require the use of physical robot model and acceleration is proposed. By utilizing neural network, the fault torque can be estimated, which allows effective fault detection and diagnosis. The feasibility of the proposed fault detection algorithm is validated through various simulations and experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Dixon, W.E., Walker, I.D., Dawson, D.M., Hartranft, J.P.: Fault detection for robot manipulators with parametric uncertainty: a prediction error based approach. In: Proceeding of the IEEE International Conference on Robotics and Automation, pp. 3628–3634 (2000)

  2. Luca, A.D., Schaffer, A.A., Hadaddin, S., Hirzinger, G.: Collision detection and safe reaction with the DLR-III lightweight manipulator arm. In: Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623–1630 (2006)

  3. Luca, A.D., Mattone, R.: Actuator failure detection and isolation using generalized momenta. In: Proceeding of the IEEE International Conference on Robotics and Automation, pp. 634–639 (2003)

  4. Lee, S.D., Song, J.B.: Sensorless collision detection based on friction model for a robot manipulator. Int. J. Precise. Eng. Man 17(1), 11–17 (2016)

    Article  Google Scholar 

  5. Caldas, A., Makarov, M., Grossard, M., Ayerbe, P.R., Dumur, D.: Adaptive residual filtering for safe human-robot collision detection under modeling uncertainties. In: Proceeding of the IEEE/ASME International Conference on Advanced Mechatronics, pp. 722–727 (2013)

  6. Ferrari, R.M.G., Parisini, T., Polycarpou, M.M.: A robust fault detection and isolation scheme for a class of uncertain input-output discrete-time nonlinear system. In: American Control Conference, pp. 2804–2809 (2008)

  7. Capisani, L.M., Ferrara, A., Loza, A.F.D., Fridman, L.M.: Manipulator fault diagnosis via higher order sliding mode observers. IEEE. T. Ind. Electron 59(10), 3979–3986 (2012)

    Article  Google Scholar 

  8. Mondal, S., Chakraborty G., Bhattacharyya, K.: Unknown input high gain observer for fault detection and isolation of uncertain systems. Eng. Lett. 17(2), 121–127 (2009)

  9. Luca, A.D., Mattone, R.: An adapt-and-detect actuator FDI scheme for robot manipulators. In: Proceeding of the IEEE International Conference on Robotics and Automation, pp. 4975–4980 (2004)

  10. Jung, S., Hsia, T.C.: Neural network inverse control techniques for PD controlled robot manipulator. Robotica 18, 305–314 (2000)

    Article  Google Scholar 

  11. Jung, S., Hsia, T.C.: Neural network impedance force control of robot manipulator. IEEE. T. Ind. Electron 45, 451–461 (1998)

    Article  Google Scholar 

  12. Ziang, Z. H., Ishida, T., Sunawada, M.: Neural network aided dynamic parameter identification of robot manipulators. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp 3298–3303 (2006)

  13. Bingul, Z., Ertunc, H.M., Oysu, C.: Comparison of inverse kinematics solutions using neural network for 6R robot manipulator with offset. ICSC Congress Comput. Intell. Methods Appl. https://doi.org/10.1109/CIMA.2005.1662342 (2005)

  14. Lewis, F.L., Yesildirak, A., Jagannathan, S.: Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor & Francis, Inc., Bristol (1998)

    Google Scholar 

  15. Vemuri, A.T., Polycarpou, M.M., Diakourtis, S.A.: Neural network based fault detection in robotic manipulators. IEEE T. Robotic. Autom. 14(2), 342–348 (1998)

    Article  Google Scholar 

  16. Vemuri, A.T., Polycarpou, M.M.: A methodology for fault diagnosis in robotic systems using neural networks. Robotica 22(4), 419–438 (2004)

    Article  Google Scholar 

  17. Terra, M.H., Tinos, R.: Fault detection and isolation in robotic manipulators via neural network. J. Field Robot 18(7), 357–374 (2001)

    MATH  Google Scholar 

  18. Eski, I., Erkaya, S., Savas, S., Yildirim, S.: Fault detection on robotic manipulators using artificial neural networks. Robot. Cim-Int. Manuf. 27(1), 115–123 (2011)

    Article  Google Scholar 

  19. Van, M., Kang, H.J.: A robust diagnosis and accommodation scheme for robot manipulators. Int. J. Control. Autom. Syst. 11(2), 377–388 (2013)

    Article  Google Scholar 

  20. Chen, M., Shi, P., Lim, C.-C.: Adaptive neural fault-tolerant control of a 3-DOF model helicopter system. IEEE Trans. Syst. Man. Cybern. Syst. 46(2), 260–270 (2016)

    Article  Google Scholar 

  21. Abbaspour, A., Yen, K.K., Noei, S., Sargolzaei, A.: Detection of fault data injection attack on UAB using adaptive neural network. Procedia Comput. Sci. 95, 193–200 (2016)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics Modelling, Planning and Control. Springer, London (2009)

    Google Scholar 

  24. Chan, S.P.: A disturbance observer for robot manipulators with applications to electronic components assembly. IEEE Trans. Ind. Electron 42(5), 487–493 (1995)

    Article  Google Scholar 

  25. Lee, K., Lee, C.-H., Hwang, S., Choi, J., Bang, Y.: Power-assisted wheelchair with gravity and friction compensation. IEEE Trans. Ind. Electron 63(4), 2203–2211 (2016)

    Article  Google Scholar 

  26. EtherCAT technology group: IEC 61800-7 ETG implementation guideline for their cia402 drive profile (2007)

Download references

Acknowledgements

This research was supported by Korea Electrotechnology Research Institute(KERI) Primary research program through the National Research Council of Science & Technology(NST) funded by the Ministry of Science, ICT and Future Planning (MSIP) (No. 17-12-N0101-22)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong Ju Kim.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(WMV 11.5 MB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, C.N., Hong, J.T. & Kim, H.J. Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators. J Intell Robot Syst 95, 137–147 (2019). https://doi.org/10.1007/s10846-018-0781-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0781-0

Keywords

Navigation