A linearized robust model predictive control applied to bioprocess

Abstract : This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameters uncertainties. First, this work focuses on the design of classical robust nonlinear model predictive control (RNMPC) law subject to model parameters uncertainties implying solving min-max optimization problem. Secondly, a new approach is proposed, consisting in approaching the basic min-max problem into a more tractable optimization problem based on the use of linearization techniques, to ensure a good trade-off between tracking accuracy and computation time. The robust stability of the closed-loop system is addressed. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC controller. Its efficiency is illustrated through numerical results and robustness against parameter uncertainties.
Type de document :
Communication dans un congrès
IEEE 55th Conference on Decision and Control (CDC), Dec 2016, Las Vegas, United States. 〈10.1109/cdc.2016.7798882 〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01637682
Contributeur : Didier Dumur <>
Soumis le : vendredi 17 novembre 2017 - 17:47:30
Dernière modification le : jeudi 1 février 2018 - 16:56:56

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Seif Eddine Benattia, Sihem Tebbani, Didier Dumur. A linearized robust model predictive control applied to bioprocess. IEEE 55th Conference on Decision and Control (CDC), Dec 2016, Las Vegas, United States. 〈10.1109/cdc.2016.7798882 〉. 〈hal-01637682〉

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