Improved, user-friendly initialization for the identification of the nonlinear LFR block-oriented model

Nowadays, there is a high need for accurate, parsimonious nonlinear dynamic models. Block-oriented nonlinear model structures are known to be excellent candidates for this task. The nonlinear LFR (Linear Fractional Representation) model, composed of a static nonlinearity (SNL) and a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) part, is highly flexible since it creates an arbitrary MIMO-LTI interconnection between the model's in- and output and the SNL's in- and output. It can create nonlinear feedback (which is very important in oscillators and mechanical applications), incorporates e.g. the Wiener-Hammerstein model as a special case and does not postulate the SNL's location prior to the identification. Starting from 2 classical frequency response measurements of the system, the method generates the best possible MIMO-LTI configuration and estimates the SNL in an automated, user-friendly, and efficient way. The resulting model parameters are fine-tuned via a subsequent optimization. The method will be illustrated via simulation experiments.