Abstract
The application of nonlinear model predictive control (NMPC) for the temperature control of an industrial batch polymerization reactor is illustrated. A real-time formulation of the NMPC that takes computational delay into account and uses an efficient multiple shooting algorithm for on-line optimization problem is described. The control relevant model used in the NMPC is derived from the complex first-principles model and is fitted to the experimental data using maximum likelihood estimation. A parameter adaptive extended Kaiman filter (PAEKF) is used for state estimation and on-line model adaptation. The performance of the NMPC implementation is assessed via simulation and experimental studies.
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Keywords
- Model Predictive Control
- Prediction Horizon
- Nonlinear Model Predictive Control
- Computational Delay
- Dynamic Matrix Control
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References
Diehl, M.: Real-Time Optimization for Large Scale Nonlinear Processes. PhD Thesis, University of Heidelberg, Heidelberg (2001)
Biegler, L.: Efficient solution of dynamic optimization and NMPC problems. In: F. Allgoewer and A. Zheng, (eds), Nonlinear Predictive Control. Birkhauser, Bagel (2000)
Qin S.J., Badgwell, T.: A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice, 11, 733–764 (2003)
Morari, M., Lee, J.H.: Model predictive control: Past, present and future. In Proc. PSE’97-ESCAPE-7 Symposium, Trondheim, (1997)
Henson, A.M.: Nonlinear model predictive control: current status and future directions. Comp. Chem. Eng., 23, 187–201 (1998)
Findeisen, R., Allgoewer, F.: Nonlinear model predictive control: From theory to application. In Int. Symp. on Design, Operation and Control of Chemical Plants (PSE Asia’02), Taipei, Taiwan (2002)
Franke, R., Arnold, E. Linke H.: HQP: A solver for nonlinearly constrained large-scale optimization, http://hqp.sourceforge.net
Biegler, L.T., Rawlings, J.B.: Optimisation approaches to nonlinear model predictive control. Proceedings of Conf. Chemical Process Control, South Padre Island, Texas, 543–571 (1991)
Valappil, J., Georgakis, C: Systematic estimation of state noise statistics for Extended Kaiman Filters. AIChE J., 46, 292–308 (2000)
Nagy, Z.K., Braatz, R.D.: Robust nonlinear model predictive control of batch processes. AIChE J., 49, 1776–1786 (2003)
Helbig, A., Abel, O., M’hamdi, A., Marquardt, W.: Analysis and nonlinear model predictive control of the Chylla-Haase Benchmark problem. Proceedings of UKACC International Conf. on Control 1172–1177 (1996)
Van Overschee, P., Van Brempt, W.: polyPROMS IPCOS-ISMC Final Report, Internal Report of the polyPROMS European 5th Framework Research Project GRD1-2000-25555.
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Nagy, Z.K., Mahn, B., Franke, R., Allgöwer, F. (2007). Real-Time Implementation of Nonlinear Model Predictive Control of Batch Processes in an Industrial Framework. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_38
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DOI: https://doi.org/10.1007/978-3-540-72699-9_38
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