Modelling the Technological Part of a Line by Use of Neural Networks

  • Jaroslava Žilková
  • Peter Girovský
  • Mišél Batmend
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)

Abstract

The paper deals with the applications of artificial neural networks in modelling and control of a continuous tinning line’s technological part. In the conclusion part of the paper, description of the whole model of the tinning line technological section together with the neural speed estimators is presented, along with an evaluation of the achieved simulation results. Training of individual neural networks was performed off-line and adaptation of the network parameters was done by Levenberg-Marquardt’s modification of the back-propagation algorithm. The DC drives were simulated in program Matlab with Simulink toolbox and neural networks were proposed in the Matlab environment by use of Neural Networks Toolbox.

Keywords

Mathematical model technological line control DC motor neural network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Company documentation ARTEP VSŽ, k. p. Košice (1999)Google Scholar
  2. 2.
    Vas, P.: Artificial-Intelligence-based electrical machines and drives. Oxford University Press, Oxford (1999)Google Scholar
  3. 3.
    Timko, J., Žilková, J., Balara, D.: Artificial neural networks applications in electrical drives, p. 239. TU of Košice (2002) (in Slovak)Google Scholar
  4. 4.
    Nitu, E., Iordache, M., Marincei, L., Charpentier, I., Le Coz, G., Ferron, G., Ungureanu, I.: FE-modeling of cold rolling by in-feed method of circular grooves. Strojniski Vestnik – Journal of Mechanical Engineering 57(9), 667–673 (2011)CrossRefGoogle Scholar
  5. 5.
    Timko, J., Žilková, J., Girovský, P.: Modelling and control of electrical drives using neural networks, p. 202. C-Press, Košice (2009) (in Slovak)Google Scholar
  6. 6.
    Brandštetter, P.: AC drives - Modern control methods. VŠB-TU Ostrava (1999)Google Scholar
  7. 7.
    Levin, A.U., Narendra, K.S.: Control of Nonlinear Dynamical Systems Using Neural Networks: Controllability and Stabilization. IEEE Transactions on Neural Networks 4, 192–206 (1993)CrossRefGoogle Scholar
  8. 8.
    Levin, A.U., Narendra, K.S.: Control of Nonlinear Dynamical Systems Using Neural Networks- Part II: Observability, Identification and Control. IEEE Transactions on Neural Networks 7, 30–42 (1996)CrossRefGoogle Scholar
  9. 9.
    Hagan, M.T., Demuth, H.B., De Jesús, O.: An introduction to the use of neural networks in control systems. International Journal of Robust and Nonlinear Control 12, 959–985 (2002)MATHCrossRefGoogle Scholar
  10. 10.
    Perduková, D., Fedor, P., Timko, J.: Modern methods of complex drives control. Acta Technica CSAV 49, 31–45 (2004)Google Scholar
  11. 11.
    Vittek, J., Dodds, S.J.: Forced dynamics control of electric drives. ZU, Žilina (2003)Google Scholar
  12. 12.
    Žilková, J.: Artificial neural networks in process control, p. 50. TU of Košice, Košice (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jaroslava Žilková
    • 1
  • Peter Girovský
    • 1
  • Mišél Batmend
    • 1
  1. 1.Department of Electrical Engineering and MechatronicsTechnical University of KošiceKošiceSlovak Republic

Personalised recommendations