Grey-box model identification – control relevant approach

Grey-box modeling is an advantageous tool for system identification when obtained input/output experimental data are insufficiently excited. The lack of information in the data can be often replaced with some additional knowledge about the modeled system, which constricts the class of models under consideration. The real system is usually more complex and do not fit the model class, thus a bias error occurs. The main goal of this paper is to show an effective way how to identify grey-box models, which would be relevant in commissioning predictive control.