The performance of most bioprocesses can be improved significantly by the application of model-based methods from advanced process control (APC). However, due to the complexity of the processes and the limited knowledge of them, plant-model mismatch is unavoidable. A variety of different modeling strategies (each with individual advantages and deficiencies) can be applied, but still, the confidence in a single process model is often low; therefore, the application of classical APC is difficult. In order to operate under possible plant model mismatch, a robust closed-loop optimizing control strategy was developed in which the mismatch is counteracted by an adaptive model correction and the parallel usage and evaluation of structurally different models. Robust multistage nonlinear model predictive control is used for the online optimization of the process trajectories in order to maximize the performance. The adapted, structurally different models are used herein as weighted scenarios for the prediction of the process, which account for structural uncertainties. It is shown in simulation studies of a CHO cultivation process that the usage of multiple, adapted models as scenarios improves (1) the accuracy of the state estimation and (2) the overall process performance.