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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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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