Nonlinear Model Predictive Control for regulation of microalgae culture in a continuous photobioreactor

The objective of this study is to design a Nonlinear Model Predictive Controller for a microalgae culture process to regulate the biomass concentration at a chosen setpoint. The optimization problem is discretized and transformed into a nonlinear programming problem, solved by Control Vector Parametrization technique. However, the performances of the NMPC usually decrease when the true plant evolution deviates significantly from that predicted by the model. Therefore, a control approach that considers model uncertainty is further considered by adding a system-model error signal which represents the gap between the system output and the model prediction. In order to reduce the influence of measurement noise introduced by sensors and to have a smooth control signal, a penalty term on the control variation is added in the objective function. Finally, the method is validated in simulation and numerical results are given to illustrate the efficiency of the control strategy for setpoint tracking in the presence of parameter uncertainties, measurement noise and light variation.