Skip to main content
SpringerLink
Log in

Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle

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

Abstract

A novel fault diagnosis and accommodation method for unmanned underwater vehicles thruster is presented in this paper. FCA-CMAC (Credit Assignment-based Fuzzy Cerebellar Model Articulation Controllers) neural network is used to realize the fault identification for thruster continuous and uncertain jammed fault situation. A reconstruction algorithm based on weighted pseudo-inverse is used to find the available solution of the control allocation problem. To illustrate effective of the proposed method, two simulation examples of multi-uncertain abrupt faults are given in the paper.

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

Access this article

Log in via an institution

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. Gianluca, A.: Underwater robots motion and force control, pp. 78–93. Springer, Berlin (2006)

    Google Scholar 

  2. Yoerger, D.G., Slotine, J.J.E.: Adaptive sliding control of an experimental underwater vehicle. Proceedings of IEEE International Conference on Robotics and Automation, pp. 2746–2751. California, USA (1991)

  3. Yang, K.C., Yuh, J., Choi, S.K.: Experimental study of fault-tolerant system design for underwater robots. Proceedings of the IEEE international conference on Robotics and Automation. Leuven, Belgium (1998)

    Google Scholar 

  4. Yang, K.C., Choi, S.K.: Fault-tolerant system design of an autonomous underwater vehicle-ODIN: an experimental study. Int. J. Syst. Sci. 30(9), 1011–1019 (1999)

    Article  MATH  Google Scholar 

  5. Podder, T.K., Sarkar, N.: Fault-tolerant decomposition of thruster forces of an autonomous underwater vehicle. Proceedings of the IEEE international conference on Robotics and Automation Detroit. MIT, USA (1999)

    Google Scholar 

  6. Podder, T.K., Antonelli, G., Sarkar, N.: Fault tolerant control of an autonomous underwater vehicle under thruster redundancy: Simulation and experiments. Proceedings of IEEE International Conference on Robotics and Automation. San Francisco, USA, pp. 276–285 (2000)

  7. Podder, T.K., Sarkar, N.: Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy. Robot. Auton. Syst. 34(1), 39–52 (2001)

    Article  Google Scholar 

  8. Cuadrado, A.A., Diaz, I.: Fuzzy inference map for condition monitoring with self-organizing maps. International Conference fuzzy logic and technology, pp. 55–58. Leicester, UK (2001)

  9. Edin, O., Geoff, R.: Thruster fault diagnosis and accommodation for open-frame underwater vehicles. Cont. Eng. Pract. 12(12), 1575–1598 (2004)

    Article  Google Scholar 

  10. Fossen, T.I.: Guidance and control of ocean vehicles. Wiley, Chichester (1995)

    Google Scholar 

  11. Tondel, P., Johansen, T.A., Bemporad, A.: Evaluation of piecewise affine control via binary search tree. Automatic 39(3), 743–749 (2003)

    Article  MathSciNet  Google Scholar 

  12. Zhu, D.Q., Kong, M.: A fuzzy CMAC neural network model based on credit assignment. Int. J. Inf. Tech. 12(6), 1–8 (2006)

    Google Scholar 

  13. Zhu, D.Q., Kong, M.: Adaptive fault-tolerant control of nonlinear system: an improved CMAC based fault learning approach. Int. J. Control 80(10), 1576–1594 (2008)

    Article  MathSciNet  Google Scholar 

  14. Johansen, T.A., Fossen, T.I., Tndel, P.: Efficient optimal constrained control allocation via multi-parametric programming. AIAA Journal of Guidance, Control and Dynamics (2002)

  15. Fossen, T.I.: Marine control systems. Marine Cybernetics AS, Trondheim (2002)

  16. Zhu, D.Q., Liu, Q., Yang, Y.S.: An active fault-tolerant control method of unmanned underwater vehicles with continuous and uncertain faults. Int. J. Adv. Robot. Syst. 5(4), 411–418 (2008)

    Google Scholar 

  17. Nie, J., Linkens, D.: FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity. Automatic 30(4), 655–664 (1994)

    Article  MATH  Google Scholar 

  18. Shun, F.S., Ted, T., Hung, T.H.: Credit assigned CMAC and its application to online learning robust controllers. IEEE Trans Syst. Man Cybern. B Cybern. 33(2), 202–213 (2003)

    Article  Google Scholar 

  19. Omerdic, E., Roberts, G.N., Ridao, P.: Fault detection and accommodation for ROVs. Sixth IFAC conference on manoeuvring and control of marine craft. MCMC, Girona (2003)

  20. Lingli, N.: Fault-tolerant control of unmanned underwater vehicles. Ph.D. Dissertation, Blacksburg, Virginia (2001)

Download references

Author information

Authors and Affiliations

  1. Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Pudong Avenue 1550, Shanghai, 200135, China

    Qian Liu & Daqi Zhu

  2. Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON, N1G2W1, Canada

    Daqi Zhu & Simon X. Yang

Authors
  1. Qian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daqi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon X. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daqi Zhu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, Q., Zhu, D. & Yang, S.X. Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle. J Intell Robot Syst 66, 463–475 (2012). https://doi.org/10.1007/s10846-011-9602-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-011-9602-4

Keywords

Search

Navigation