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.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:53337", author = "H. Zabiri and N. Mazuki", title = "A Black-Box Approach in Modeling Valve Stiction", abstract = "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 = "Control valve stiction, neural network, modeling.", volume = "4", number = "8", pages = "606-8", }
