Robust Nonlinear Model Predictive Controller based on sensitivity analysis — Application to a continuous photobioreactor

Abstract

This paper deals with the design of a predictive control law for microalgae culture process to regulate the biomass concentration at a chosen setpoint. However, the performances of the Nonlinear Model Predictive Controller usually decrease when the true plant evolution deviates significantly from that predicted by the model. Thus, a robust criterion under model's parameters uncertainties is considered, implying solving a min-max optimization problem. In order to reduce the computational burden and complexity induced by this formulation, a sensitivity analysis is carried out to determine the most influential parameters which will be considered in the optimization step. The proposed approach is validated in simulation and numerical results are given to illustrate its efficiency for setpoint tracking in the presence of parameters uncertainties.