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A linearized robust model predictive control applied to bioprocess

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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.
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Dates and versions

hal-01637682 , version 1 (12-03-2020)

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