Neural-Network Based Robust FTC: Application to Wind Turbines

  • Marcel Luzar
  • Marcin Witczak
  • Józef Korbicz
  • Piotr Witczak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)


The paper deals with the problem of a robust fault diagnosis of a wind turbine. The preliminary part of the paper describes the Linear Parameter-Varying model derivation with a Recurrent Neural Network. The subsequent part of the paper describes a robust fault detection, isolation and identification scheme, which is based on the observer and \(\mathcal{H}_{\infty}\) framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error while guaranteeing the convergence of the observer. Moreover, the controller parameters selection method of the considered system is presented. Final part of the paper shows the experimental results regarding wind turbines, which confirms the effectiveness of proposed approach.


Fault diagnosis fault identification robust control fault-tolerant control neural networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    De Oca, S., Puig, V., Witczak, M., Dziekan, L.: Fault-tolerant control strategy for actuator faults using lpv techniques: application to a two degree of freedom helicopter. International Journal of Applied Mathematics and Computer Science 22(1), 161–171 (2012)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Gillijns, S., De Moor, B.: Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica 43, 111–116 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Iserman, R.: Fault Diagnosis Applications: Model Based Condition Monitoring, Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems. Springer, Berlin (2011)CrossRefGoogle Scholar
  4. 4.
    Lachhab, N., Abbas, H., Werner, H.: A neural-network based technique for modelling and LPV control of an arm-driven inverted pendulum. In: Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico, pp. 3860–3865 (2008)Google Scholar
  5. 5.
    Luzar, M., Czajkowski, A., Witczak, M., Mrugalski, M.: Actuators ans sensors fault diagnosis with dynamic, state-space neural networks. In: Methods and Models in Automation and Robotics - MMAR 2012: Proceedings of the 17th IEEE International Conference, pp. 196–201 (2012)Google Scholar
  6. 6.
    Luzar, M., Witczak, M., Witczak, P.: Robust \(\mathcal{H}_{\infty}\) actuator fault diagnosis with neural network. In: Methods and Models in Automation and Robotics - MMAR 2013: Proceedings of the 18th IEEE International Conference, pp. 200–205 (2013)Google Scholar
  7. 7.
    Oliveira, M., Bernussou, J., Geromel, J.: A new discrete-time robust stability condition. System and Control Letters 37(4), 261–265 (1999)CrossRefzbMATHGoogle Scholar
  8. 8.
    Puig, V.: Fault diagnosis and fault tolerant control using set-membership approaches: Application to real case studies. International Journal of Applied Mathematics and Computer Science 20(4), 619–635 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Sloth, C., Esbensen, T., Stoustrup, J.: Robust and fault-tolerant linear parameter-varying control of wind turbines. Mechatronics 21(4), 645–659 (2011)CrossRefGoogle Scholar
  10. 10.
    Witczak, M.: Modelling and Estimation Strategies for Fault Diagnosis of Non-linear Systems. Springer, Berlin (2007)zbMATHGoogle Scholar
  11. 11.
    Witczak, M., Puig, V., Montes De Oca, S.: A fault-tolerant control strategy for non-linear discrete-time systems: application to the twin-rotor system. International Journal of Control 86(10), 1788–1799 (2013)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Witczak, M.: Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems. Springer, Berlin (2014)CrossRefGoogle Scholar
  13. 13.
    Zemouche, A., Boutayeb, M.: Observer design for Lipschitz non-linear systems: the discrete time case. IEEE Trans. Circuits and Systems - II: Express Briefs 53(8), 777–781 (2006)CrossRefGoogle Scholar
  14. 14.
    Zemouche, A., Boutayeb, M., Iulia Bara, G.: Observer for a class of Lipschitz systems with extension to \(\mathcal{H}_{\infty}\) performance analysis. Systems and Control Letters 57(1), 18–27 (2008)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marcel Luzar
    • 1
  • Marcin Witczak
    • 1
  • Józef Korbicz
    • 1
  • Piotr Witczak
    • 1
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

Personalised recommendations