Robust Identification of Nonlinear Oscillators from Frequency Response Data

While experimental modal analysis for linear systems is well established and widely used, no universal procedure for identification of nonlinear oscillatory systems is currently available. Thus, the authors develop an automated tool to robustly identify nonlinear oscillators from data. Frequency response curves measured with periodic shaker excitation are arguably the best source to obtain a robust model for nonlinear oscillators. In this setting, an analysis in the frequency domain beneficially reduces noise and helps compress information. Then, data-driven identification techniques, curve fitting, as well as a judicious parameter elimination are combined to yield a low-order and sparse nonlinear oscillator, which can be used to accurately capture the considered system’s forced response curve.