Abstract
In this paper, multiple piecewise linearizations of a nonlinear process in different operating points are used within a Kalman filter bank which computes the conditional probabilities of various hypotheses that are modeled by the filters. State estimates provided by the Kalman Filters and local model parameters are weighted using conditional probabilities and then used within the predictive control framework. The proposed strategy is tested on the complex model of styrene polymerization process.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Kalman Filter
- Model Predictive Control
- Continuous Stir Tank Reactor
- Styrene Polymerization
- Conditional Probability Density Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Grune, L., Pannek, J.: Nonlinear Model Predictive Control. Springer, London (2011)
Murray-Smith, R., Johansen, T.A.: Multiple Model Approaches to Modelling and Control. Taylor and Francis, London (1997)
Gregorcic, G., Lightbody, G.: Nonlinear system identification: From multiple-model networks to Gaussian processes. Engineering Applications of Artificial Intelligence 21, 1035–1055 (2008)
Schley, M., Prasad, V., Russo, L.P., Bequette, B.W.: Nonlinear Model Predictive Control of a Styrene Polymerization Reactor. Progress in Systems and Control Theory 26, 403–417 (2000)
Xu, Z., Zhao, J., Qian, J.: Nonlinear MPC using identified LPV model. Industrial & Engineering Chemistry Research 48, 3043–3051 (2009)
Maner, B.R., Doyle, F.J., Ogunnaike, B.A., Pearson, R.K.: Nonlinear Model Predictive Control of a Simulated Multivariable Polymerization Reactor using second-order Volterra Models. Automatica 32, 1285–1301 (1996)
Pekar, J., Havlena, V.: Control of CSTR using model predictive controller based on mixture distribution. In: Proceedings of the 6th IFAC Symposium on Nonlinear Control Systems (2004)
Rupp, D., Ducard, G., Shafai, E., Geering, H.P.: Extended Multiple Model Adaptive Estimation for the Detection of Sensor and Actuator Faults. In: Proceedings of the IEEE Conference on Decision and Control, pp. 3079–3084 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Novák, J., Chalupa, P. (2013). Multiple Model Predictive Control of a Styrene Polymerization Process. In: Zelinka, I., Rössler, O., Snášel, V., Abraham, A., Corchado, E. (eds) Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems. Advances in Intelligent Systems and Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33227-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-33227-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33226-5
Online ISBN: 978-3-642-33227-2
eBook Packages: EngineeringEngineering (R0)