System Identification of Dynamic Systems with Cubic Nonlinearities Using Linear Time-periodic Approximations

This work develops methods to identify parametric models of nonlinear dynamic systems from response measurements using tools for Linear Time Periodic (LTP) systems. The basic approach is to drive the system periodically in a stable limit cycle and then measure deviations of the response from that limit cycle. Under certain conditions, the resulting response can be well approximated as that of a linear-time periodic system. In the analytical realm it is common to linearize a system about a periodic trajectory and then use Floquet analysis to assess the stability of the limit cycle. This work is concerned with the inverse problem, using a measured time-periodic response to derive a nonlinear dynamic model for the system. Recently, a few new methods were developed that facilitate the experimental identification of linear time periodic systems, and those methods are exploited in this work. The proposed system identification methodology is evaluated by applying it to a Duffing oscillator, demonstrating that the nonlinear forcedisplacement relationship can be identified without a priori knowledge of its functional form. The proposed methods are also applied to simulated measurements from a cantilever beam with a cubic nonlinear spring on its tip, revealing that the model order of the system and the displacement dependent stiffness can be readily identified.