Identification of Nonlinear Stiffness and Damping Parameters Using a Hybrid Approach

Robustly identifying nonlinear mechanical systems is generally a challenging task, and this is particularly true when the structure under test exhibits nonlinear behaviors related to both stiffness and damping. In this work, a hybrid nonlinear identification approach is proposed by combining the restoring force surface (RFS) method and the nonlinear subspace identification method. The multiparameter nonlinear identification strategy is based on a first characterization conducted using the RFS method, followed by a nonlinear state-space representation using subspace algorithms. Two common friction simulation examples and one complex multi-degree-of-freedom system are employed to verify the proposed method. The effect of the measurement noise on the parameter estimation results is investigated by corrupting the previously generated output, adding different levels of Gaussian zero-mean noise. Results show that the nonlinear coefficients associated with the stiffness and damping nonlinearities can be identified with a high level of confidence, and the proposed method works well under different noise-level contaminations.