Robustification of nonlinear model predictive control

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.

Seif Eddine Benattia received the Ph.D. degree from CentraleSupélec, France, in 2016. Since 2013, he has been with the Signal and Systems Laboratory in the Automatic Control Department. His current research deals with robust predictive methods for control and applications to bioprocesses (modelling and control).