Nonlinear dynamic system identification for automotive crash using optimization: A review

Abstract.

Literature on linear and nonlinear dynamic system identification is reviewed. The main motivation is to document the state-of-the-art, allowing one to propose further advancements in this field. The main problem is to identify system properties when experimental/numerical input and output data are specified. Parametric as well as nonparametric approaches for system identification are reviewed. For linear systems, both the first order and second order forms of the equations of motion are discussed. The use of first order form is more general as it can treat nonproportional structural damping as well. For nonlinear systems, the second order form of the equations of motion is used. A conclusion from the study is that more work is needed to develop identification formulations for nonlinear dissipative dynamic systems, especially for multi-degree of freedom systems.