Abstract
The Nonlinear Identification through Feedback of the Output (NIFO) and Conditioned Reverse Path (CRP) methods are a popular family of approaches for nonlinear system identification. They estimate the underlying linear Frequency Response Function (FRF) as well as the parameters describing the mechanical system’s nonlinearities. However, one troubling aspect is that the parameters obtained are complex numbers and typically are found to vary with frequency, so post-processing must be employed to obtain physically reasonable parameters and an accurate estimate of the underlying FRFs. This work proposes two methods (based on the H1 and H2 algorithms) which can be used in the estimation of the linear FRF as well as frequency-independent nonlinear parameters. This paper evaluates the methods numerically using a single degree of freedom system and exploring various methods for determining which nonlinear parameters to include in the model.
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