Iterative Model Identification of Nonlinear Systems of Unknown Structure: Systematic Data-Based Modeling Utilizing Design of Experiments

High-quality models are essential to the performance of many control-related tasks [1]-[3]. If the structure of the system is known, first principle models can be created (which constitutes the best choice for most uses), especially if they should be used as design tools for parametric studies without having to build the corresponding hardware. However, first principle modeling is hardly possible for many real systems, either because the detailed knowledge of the system structure is not available or the model would be too complex to be useful for control design or to be parameterized. It has become common to use data-driven models, that is, correctly reproducing the input-output behavior of the system without trying to correctly describe its physics. For linear systems, data-driven modeling has been intensively studied, and powerful tools exist [4].