DocumentCode :
184323
Title :
Economic multi-stage output nonlinear model predictive control
Author :
Subramanian, Sivaraman ; Lucia, Sergio ; Engell, Sebastian
Author_Institution :
Dept. of Biochem. & Chem. Eng., Tech. Univ. Dortmund, Dortmund, Germany
fYear :
2014
fDate :
8-10 Oct. 2014
Firstpage :
1837
Lastpage :
1842
Abstract :
Nonlinear Model Predictive control is one of the most promising control strategies in the field of advanced control. It can be used to optimize economic cost functions online satisfying all constraints which makes it very appealing in the context of industrial applications. In the last years, several robust NMPC methods have been presented. Among them, multi-stage stochastic NMPC has been proven to provide very promising results and to be computationally feasible by the use of advanced optimization tools. In this paper, we present an extension of the multi-stage approach that takes into account explicitly not only plant-model mismatch but also state estimation error through innovation sampling. We accommodate these errors into the resulting optimization problem by including them in the scenario tree formulation. We use a multiple-model estimation algorithm that fits to the multi-stage approach. The results are illustrated by simulation results of a chemical reactor.
Keywords :
chemical reactors; nonlinear control systems; optimisation; predictive control; state estimation; chemical reactor; economic multistage output nonlinear model predictive control; explicit analysis; innovation sampling; multiple-model estimation algorithm; multistage stochastic NMPC; online economic cost function optimization; plant-model mismatch; robust NMPC methods; scenario tree formulation; state estimation error; Estimation; Mathematical model; Noise; Optimization; Robustness; Technological innovation; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Applications (CCA), 2014 IEEE Conference on
Conference_Location :
Juan Les Antibes
Type :
conf
DOI :
10.1109/CCA.2014.6981580
Filename :
6981580
Link To Document :
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