State estimation and predictive control of fed-batch cultures of hybridoma cells

• Application of nonlinear model predictive control (NMPC) to hybridoma cells.
• Observation of non-measurable states (glucose, glutamine) using unscented Kalman filter (UKF).
• Robustness to model uncertainties thanks to a minimax original NMPC algorithm.
• Combination of the closed-loop NMPC using UKF outputs.

Fed-batch cultures of hybridoma cells are commonly used for the production of monoclonal antibodies (MAb). In this study, a simple macroscopic model of the cell culture is used, which is based on the overflow metabolism paradigm. This allows to specify optimal culture conditions, and the natural formulation of a nonlinear model predictive control strategy (NMPC). As not all the component concentrations are available for measurement, system observability is analyzed, and an unscented Kalman filter (UKF) is designed, which provides satisfactory estimates of glucose and glutamine concentrations. Robustness of the NMPC scheme is investigated, as well as the combined UKF+NMPC scheme, through a minimax robust version and the closed-loop system.