Browse > Article

Bilinear Inverse Model Predictive Control for Grade Change Operations Based on Artificial Neural Network  

Choo, Yeon-Uk (Department of Chemical Engineering, Hanyang University)
Kim, Joon-Yeol (Department of Chemical Engineering, Hanyang University)
Yeo, Yeong-Koo (Department of Chemical Engineering, Hanyang University)
Kang, Hong (J. J. Engineering)
Publication Information
Journal of Korea Technical Association of The Pulp and Paper Industry / v.37, no.1, 2005 , pp. 67-72 More about this Journal
Abstract
In the grade change operations inputs and outputs are highly correlated and application of conventional linear feedback control methods such as PID schemes might lead to poor control performance. In this study the neural networks model for the grade change operation is trained by using bilinear terms which can represent non-linear characteristics of grade change operations. The inverse model of the grade change operation is obtained from training and the optimal input variables are computed from the trained neural networks as well. The proposed bilinear inverse model predictive control scheme was found out to showlittle discrepancy between simulated outputs and setpoints.
Keywords
Citations & Related Records
연도 인용수 순위
  • Reference
1 Mohler, R. R., Bilinear Control Process, Academic Press, New York, (1973)
2 Zurada, J. M., Introduction to artificial neural systems, PWS Publishing Company, Boston, (1992)
3 Ruberti, A., A.Isidori, and P. D'Alessandro, Theory of Bilinear Dynamical Systems, Springer-Verlag, (1972)
4 Hornik, K., Stinchcombe, M., and White, H.: 'Multilayer feedforward networks are universal approximators', Neural Networks, 2:359 (1989)   DOI   ScienceOn
5 Jordan, M. I., and Rumelhart, D. E.: 'Forward models: supervised learning with a distal teacher', Occasional paper 40, Center for Cognitive Science, MIT, (1991)