Abstract
A novel robust controller, chance constrained nonlinear MPC, is presented. Time-dependent uncertain variables are considered and described with piecewise stochastic variables over the prediction horizon. Restrictions are satisfied with a user-defined probability level. To compute the probability and its derivatives of satisfying process restrictions, the inverse mapping approach is extended to dynamic chance constrained optimization cases. A step of probability maximization is used to address the feasibility problem. A mixing process with both an uncertain inflow rate and an uncertain feed concentration is investigated to demonstrate the effectiveness of the proposed control strategy.
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Keywords
- Model Predictive Control
- Feasibility Problem
- Uncertain Variable
- Propose Control Strategy
- Chance Constraint
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© 2007 Springer-Verlag Berlin Heidelberg
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Xie, L., Li, P., Wozny, G. (2007). Chance Constrained Nonlinear Model Predictive Control. 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_23
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DOI: https://doi.org/10.1007/978-3-540-72699-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72698-2
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