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Application of ICT in Multivariable System Identification of Cement Mill Process

Author(s) : P S Godwin Anand, R Krishna Priya and P Subbaraj

Volume & Issue : VOLUME 2 / 2015 , ISSUE 2

Page(s) : 39-46
ISSN (Online): 2394-3858
ISSN (Print) : 2394-3866

Abstract

This paper deals with the application of ICT in identification of multivariable cement mill process using Non-linear Autoregressive with Exogenous Inputs (NARX) models with wavelet network using MATLAB system identification toolbox. NARX identification, based on a sequence of input/output samples, collected from a real cement mill process is used for black-box modeling of non-linear cement mill process. The NARX model is considered for two inputs and two outputs of seven hours of data with sample time of five seconds. In order to assess the suitability of identified model, Model validation tests are performed by means of auto-correlation function and cross-correlation function. The fitness of NARX identified model is compared with ARX model. The identified NARX model is converted to discrete transfer function with the help of the MATLAB system identification toolbox and the dynamic characteristic of the identified model are evaluated and results are given.



Keywords

, MATLAB, System identification, Cement mill, NARX.

References

  1. Ljung, L. 1999, System Identification: Theory for the User, Second Edition, Prentice Hall, New Jersey.
  2. Chetouani, Y. 2007, “Nonlinear modelling of a reactor-exchanger by using NARX neural networks”, in Proc. of European Congress of Chemical Engineering, pp. 1-13.
  3. Ponchet,  A., Ponchet, J. L. and Moschytz, G. S. 1995, “On the input/ output approximation of nonlinear systems”, Proceeding of ISCAS-95, pp. 1500–1503. 
  4. Fortuna, L., Nunnari, G. and Gallo A. 1992, “Model order reduction techniques with applications in electrical engineering”, Springer-Verlag,
  5. Haber, R. and Unbehauen, H. 1990, “Structure identification of nonlinear dynamic system-a survey on input/output approaches,” Automatica, vol. 26, pp. 651–677.
  6. McCabe, S., Davies, P. and Seidel, D. 1991, “On the use of nonlinear autoregressive moving average models for simulation and system identification,” American Control Conference, pp. 1758–1763.
  7. Ljung, L. The system identification toolbox: the manual, The MathWorks Inc., Seventh edition, USA, 2007.
  8. Billings, S. A. and Leontaritis, I. J. 1982, “Parameter estimation techniques for non-linear system”, in Proc. of symposium on Identification and System Parameter Estimation, pp. 235-244.
  9. Leontaritis, I. J. and Billings, S. A. 1985, “Input-output parametric models for non-linear systems, part I and part II”, International Journal of Control, Vol. 41, No. 2, pp. 303-344.
  10. Chen, S., Billings, S.A. and Luo, W. 1989, “Orthogonal least squares methods and their application to nonlinear system identification”, Intl. Journal of Control, Vol. 50, pp. 1873-1896.
  11. Previdi, F. 2002, “Identification of black-box nonlinear models for lower limb movement control using functional electrical stimulation”, Control Engineering Practice, Vol. 10, pp. 91-99.
  12. Ramesh, K., Azis, N. and Abd-Shukor, S. R. 2008, “Development of NARX model for distillation column and studies on effect of regressors”, Journal of Applied sciences, Vol. 8, No. 7, pp. 1214-1220.
  13. Shojaeefard, M. H., Goudarzi, K., Noorpoor, A.R. and Fazelpour, M. 2008, “A Study of Thermal Contact using Nonlinear System Identification Models”, American Journal of Engineering and Applied Sciences, Vol. 1, No.1, pp. 16-23. 
  14. Billings, S. and Voon, W. S. F. 1986, “Correlation based model validity tests for non-linear models”, International Journal of Control, Vol. 44, pp. 235-244.
  15. Zhang, J. and Morris, J. 1996, “Process modelling and fault diagnosis using fuzzy neural networks”, Fuzzy Sets and Systems, Vol. 79, pp. 127-140.