Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30831
A Black-Box Approach in Modeling Valve Stiction

Authors: H. Zabiri, N. Mazuki


Several valve stiction models have been proposed in the literature to help understand and study the behavior of sticky valves. In this paper, an alternative black-box modeling approach based on Neural Network (NN) is presented. It is shown that with proper network type and optimum model structures, the performance of the developed NN stiction model is comparable to other established method. The resulting NN model is also tested for its robustness against the uncertainty in the stiction parameter values. Predictive mode operation also shows excellent performance of the proposed model for multi-steps ahead prediction.

Keywords: Modeling, Neural Network, Control valve stiction

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1293


[1] M. A. A. S. Choudhury, S. L. Shah, & N. F. Thornhill, Diagnosis of poor control-loop performance using higher-order statistics, Automatica, 40, 2004, 1719-1728.
[2] Choudhury, M. A. A. S., Shah, S. L., and Thornhill, N. F. (2004). Diagnosis of poor control-loop performance using higher-order statistics. Automatica, 40, pp. 1719-1728.
[3] Zabiri, H., and Samyudia, Y. (2006). A hybrid formulation and design of model predictive control for systems under actuator saturation and backlash. Journal of Process Control, 16, pp. 693-709.
[4] Choudhury, M. A. A. S., Thornhill, N. F., and Shah, S. L. (2005). Modeling valve stiction. Control Engineering Practice, 13, pp. 641-658.
[5] Muller, F. (1994). Simulation of an air operated sticky flow control valve. Proceedings of the 1994 Summer Computer Simulation Conferences, pp. 742-745.
[6] Kano, M., H., Kugemoto, H., and Shimizu, K. (2004). Practical model and detection algorithm for valve stiction. In Proceedings of the Seventh IFAC-DYCOPS Symposium, Boston, USA.
[7] Siegelmann, H. T., Horne, B. G., and Giles, C. L., Computational Capabilites of Recurrent Neural Networks, IEEE Transactions on Systems, Man. And Cybernetics - Part B: Cybernetics, 27 (2), pp. 208- 215.
[8] Choudhury, M A. A. S., Kariwala, V., Shah, S. L., Douke, H., Takada, H., and Thornhill, N. F. (2005). A simple test to confirm control valve stiction. IFAC World Congress, Praha.
[9] Hagan M.T., Demuth H.B., Beale M.H. (1996). Neural Network Design, PWS Publishing Company, Boston, MA.
[10] Radhakrishnan, V. R., Zabiri, H., and Thanh, D. V. (2006). Application of Multivariable Modeling in the Hydrocarbon Industry, International Conference on Computer Process Control, Lake Louise, Alberta Canada, January 10-16.
[11] Qahwaji, R. and T. Colak, Neural Network-based Prediction of Solar Activities, in CITSA2006: Orlando. (2006).
[12] Kim, J., Mowat, A., Poole, P., and Kasabov, N., Linear And Non-Linear Pattern Recognition Models For Classification Of Fruit From Visible- Near Infrared Spectra, Chemometrics And Intelligent Laboratory Systems, 2000. 51: pp.,201-216.
[13] Gomm, J. B., D. Williams, J. T. Evans (1996). Development of a Neural-Network Predictive Controller, Liverpool John Moores University,UK.