Within this work a nonlinear model predictive controller (MPC) was implemented in a Penicillium chrysogenum fed-batch process and compared to a PI(D) and an open loop feedback control scheme, referenced as model based control (MBC). The controllers were used to maintain predefined set-points of biomass specific glucose uptake rates, product precursor and nitrogen concentrations by manipulating the glucose, precursor and nitrogen feeds. As the critical component concentrations are not available for direct measurement a particle filter including measured oxygen uptake rate (OUR) and carbon evolution rate (CER) was deployed to estimate biomass, nitrogen and product precursor concentrations. State estimation and predictive control actions were based on a kinetic model which was retrieved from literature and adapted to the examined process and control tasks by simplifying the description of the hyphal compartmentalization and adding nitrogen as well as the measurable OUR and CER. Besides simulations, verification experiments of the developed control schemes were executed. Although the kinetic model used for state estimation and prediction does not reflect the overall biological complexity it could be successfully used to estimate and control the glucose uptake and the unmeasured component concentrations. During experimental verification, nonlinear process dynamics caused unstable PI(D) behavior. In comparison to PI(D) and MBC, the MPC efficiently avoided formation of by-products, which resulted in efficient substrate utilization and an overall product gain of 14%.