Robust Trajectory Planning and Robust NMPC of Processes with Model Uncertainties

This paper presents an approach for robust open-loop and robust closed-loop control of biological production processes described by models which comprise uncertain initial conditions and parameter uncertainties. The application of these robust techniques dramatically decreases the variability of the realized process trajectory and hence, the fluctuation of the product amount. The algorithm utilizes a second order approximation to calculate the first two statistical moments of the system's output as a function of the stochastic system's state and uncertain model parameters. Extensive simulation studies of both open- and closed-loop controlled fermentations based on a simple unstructured production model as well as on a structured compartment model consisting of 7 biotic states are shown.