Nonlinear model identification for mechanical structures is a challenging task, particularly when the structure exhibits asymmetric nonlinear behaviour related to both stiffness and damping. In this paper, a new method for parametric nonlinear model identification of structures from measured data is proposed by cascading optimisation problems defined for model selection and parameter estimation steps. Model selection is firstly carried out using an algebraic-based cost function to extend the equation of motion of the underlying linear system to include nonlinearities. Afterwards, parameter tuning is conducted by utilising a simulation-based cost function that relies on the instantaneous characteristics of the measured response. A numerical study is conducted to investigate the behaviour of the optimisation problems used for the identification. Further, the validity of the proposed method is demonstrated based on the responses of the systems with symmetric and asymmetric nonlinearities. Then, the method is successfully applied to experimental data from a pylon subassembly mockup structure in order to identify its asymmetric nonlinear damping and stiffness effects.

中文翻译：

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非线性组件的数据驱动结构识别：不对称刚度和阻尼非线性


机械结构的非线性模型识别是一项具有挑战性的任务，特别是当结构表现出与刚度和阻尼相关的不对称非线性行为时。在本文中，通过为模型选择和参数估计步骤定义的级联优化问题，提出了一种根据测量数据识别结构参数非线性模型的新方法。首先使用基于代数的成本函数来进行模型选择，以扩展基础线性系统的运动方程以包含非线性。然后，利用基于模拟的成本函数进行参数调整，该成本函数依赖于测量响应的瞬时特性。进行数值研究来研究用于识别的优化问题的行为。此外，基于对称和非对称非线性系统的响应证明了所提出方法的有效性。然后，该方法成功应用于塔架组件模型结构的实验数据，以识别其不对称非线性阻尼和刚度效应。