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Linearized min‐max robust model predictive control: Application to the control of a bioprocess

Abstract : This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameter uncertainties. First, this work focuses on the design of a robust nonlinear model predictive control (RNMPC) law subject to model parameter uncertainties implying solving a min-max optimization problem. Secondly, a new approach is proposed, consisting in relating the min-max problem to 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 developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC and nonlinear model predictive controllers. Its efficiency and its robustness against parameter uncertainties and/or perturbations are illustrated through numerical results.
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Submitted on : Thursday, March 12, 2020 - 11:41:22 AM
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Seif Eddine Benattia, Sihem Tebbani, Didier Dumur. Linearized min‐max robust model predictive control: Application to the control of a bioprocess. International Journal of Robust and Nonlinear Control, Wiley, 2019, ⟨10.1002/rnc.4754⟩. ⟨hal-02334153⟩

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