Nonlinear LFR Block-Oriented Model: Potential Benefits and Improved, User-Friendly Identification Method

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 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 input and output and the SNL´s input and output. First of all, it can cope with the nonlinear feedback (which is very important in oscillators and mechanical applications). Secondly, it incorporates certain classical block-oriented models as special cases. Finally, it does not postulate the SNL´s location prior to the identification. Starting from two 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 (noniterative) way. The method will be illustrated on simulation examples and experimental data.