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.