Advertisement

Cluster Computing

, Volume 22, Supplement 3, pp 6209–6217 | Cite as

Iterative learning control for a class of parabolic system fault diagnosis

  • Yinjun ZhangEmail author
  • Yinghui LiEmail author
  • Jianhuan Su
Article
  • 53 Downloads

Abstract

The paper focuses on the fault detection problem for a class of parabolic system. Main goal is to use iterative learning control algorithm to detect faults. Then, by constructing a novel control strategy depending on P-type learning law. In this way, the control strategy can ensure the convergence of fault error and residual signal with iterative number, the uniform convergence of the learning control algorithm is obtained from the sufficient conditions and the detail proof is given. Finally, the effectiveness of the proposed method is demonstrated by an example.

Keywords

Iterative learning control Parabolic system Fault diagnosis 

Notes

Acknowledgments

The work was supported by the Hechi University Foundation (XJ2016ZD004) and was supported by the Projection of Environment Master Foundation (2017HJA001).

Compliance with ethical standards

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. 1.
    Wen, H.-Y.: Fault Diagnosis and Fault Tolerant Control of Control System. Machinery Industry Press, Beijing (1998)Google Scholar
  2. 2.
    Zhang, Y., et al.: A class of time-delay disturbance discrete system for iterative learning control. ICIC Express Lett. Part B 7, 357–362 (2016)Google Scholar
  3. 3.
    Baniamerian, A., Khorasani, K.: Fault detection and isolation dissipative parabolic PDEs: finite-dimensional geometric approach. In: American Control Conference (ACC), pp. 5894-5899 (2012).Google Scholar
  4. 4.
    Zhang, Y., Li, Y., et al.: Vector analysis for iterative learning control algorithm. J. Comput. Theor. Nanosci. 12(12), 4724–4729 (2015)CrossRefGoogle Scholar
  5. 5.
    Jiang, B., Wang, J.L., Soh, Y.C.: An adaptive technique for robust diagnosis of faults with independent effects on system outputs. Int. J. Control 75(11), 792–802 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Jiang, B., Staroswiecki, M.: Adaptive observer design for robust fault estimation. Int. J. Syst. Sci. 33(9), 767–775 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chung, S., Park, T.S., Park, S.H., et al.: Colorimetric sensor array for white wine tasting. Sensors 15, 18197–18208 (2015)CrossRefGoogle Scholar
  8. 8.
    Acquah, G.E., Via, B.K., Billor, N., et al.: Identifying plant part composition of forest logging residue using infrared spectral data and linear discriminant analysis. Sensors 16(9), 1375 (2016)CrossRefGoogle Scholar
  9. 9.
    Xie, S.L., et al.: Theory and Application of Iterative Learning Control. Science Press, Beijing (2005)Google Scholar
  10. 10.
    Wang, Y., Zhou, D.: Two-Dimensional Model Theory and Its Application of Iterative Learning Control. Science Press, Beijing (2013)Google Scholar
  11. 11.
    Zhou, D., et al.: Fault diagnosis of dynamic systems. J. Autom. 35(6), 748–758 (2009)Google Scholar
  12. 12.
    Zhang, D.H., et al.: Fault diagnosis method of dynamic system. J. Autom. 17(2), 153–158 (2000)Google Scholar
  13. 13.
    Arimoto, S., Kawamura, S., Miyazaki, F.: Bettering operation of robots by learning. J. Robot. Syst. 1(2), 123–140 (1984)CrossRefGoogle Scholar
  14. 14.
    Su, J., Zhang, Y., et al.: Singular distributed parameter system iterative learning control with forgetting factor with time-delay. Int. J. u- e-Serv. Sci. Technol. 9(7), 182–194 (2016)Google Scholar
  15. 15.
    Wei, C.A.O., et al.: Fault diagnosis of discrete linear time varying systems based on iterative learning. Control Decis. 28(1), 137–140 (2013)Google Scholar
  16. 16.
    Wei, C.A.O., et al.: Fault diagnosis of discrete time varying systems based on angle correction iterative learning. Control Theory Appl. 29(11), 1495–1500 (2012)Google Scholar
  17. 17.
    Qi, Q.-H.: Fault estimation based on ESO iterative learning algorithm. Control Decis. 30(3), 546–550 (2015)Google Scholar
  18. 18.
    Liu, P.: Nonlinear distributed parameter system robust fault detection design. Shanghai Jiao Tong Univ. J. 45(2), 241–246 (2011)Google Scholar
  19. 19.
    Demetriou, M.A.: A model-based fault detection and diagnosis scheme for distributed parameter systems: a learning systems approach. ESAIM 7, 43–67 (2002)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Armaou, A., Demetriou, M.A.: Robust detection and accommodation of incipient component and actuator faults in nonlinear distributed processes. AIChE J. 54, 2651–2662 (2008)CrossRefGoogle Scholar
  21. 21.
    Claudio, B., Andrea, P., Lorenzo, M.: Fault tolerant control of the ship propulsion system benchmark. Control Eng. Pract. 11(4), 483–492 (2003)Google Scholar
  22. 22.
    Wang, H., Daley, S.: Actuator fault diagnosis: an adaptive observer based technique. IEEE Trans. Autom. Control 41(7), 1073–1078 (1996)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Aeronautics and Astronautics Engineering InstituteAir Force Engineering UniversityXi’anChina
  2. 2.School of Physics and Electrical EngineeringHechi UniversityYizhouChina

Personalised recommendations