Black- and white-box approaches for cascaded tanks benchmark system identification

This contribution consists of the identification and comparison of different models for a non-linear system: the Cascaded Tanks system. The identification of this system is challenging due to the combination of soft and hard non-linearities. Model structures with different levels of flexibility and prior knowledge are compared. The most simple ones are linear black-box models. They are extended to become non-linear black-box models, whose performances are compared with the linear ones. A second track is the investigation of a series of models with increasing complexity based on physical prior knowledge. Results show that while linear black-box models perform good in prediction, a fairly precise description of the non-linear effects is needed to achieve good performances in simulation. All models have been estimated and validated using benchmark data from a real cascaded tanks system. The contribution represents also an overview on how standard modelling techniques perform on a real identification problem.