Identification method for nonlinear LFR block-oriented models with multiple inputs and outputs

Recently, the nonlinear LFR model has been proposed as a candidate model with high potential, due to its surprising flexibility and parsimony. It is a quite general block-oriented model consisting of a static nonlinearity (SNL) and multiple-input-multiple-output (MIMO) dynamics. It can cope with both nonlinear feedforward and nonlinear feedback effects and does not postulate the SNL´s location prior to the identification. This contribution extends the model from single-input-single-output (SISO) to MIMO. Starting from two classical frequency response measurements of the system, the method delivers the best possible MIMO dynamics and estimates the SNL in an automated, user-friendly, non-iterative way, with an improved computational efficiency. The method is successfully applied on a numerical simulation example to illustrate the theory.