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An Optimization-Based Framework for Nonlinear Model Selection and Identification

机译:基于优化的非线性模型选择和识别框架

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This paper proposes an optimization-based framework to determine the type of nonlinear model present and identify its parameters. The objective in this optimization problem is to identify the parameters of a nonlinear model by minimizing the differences between the experimental and analytical responses at the measured coordinates of the nonlinear structure. The application of the method is demonstrated on a clamped beam subjected to a nonlinear electromagnetic force. In the proposed method, the assumption is that the form of nonlinear force is not known. For this reason, one may assume that any nonlinear force can be described using a Taylor series expansion. In this paper, four different possible nonlinear forms are assumed to model the electromagnetic force. The parameters of these four nonlinear models are identified from experimental data obtained from a series of stepped-sine vibration tests with constant acceleration base excitation. It is found that a nonlinear model consisting of linear damping and linear, quadratic, cubic, and fifth order stiffness provides excellent agreement between the predicted responses and the corresponding measured responses. It is also shown that adding a quadratic nonlinear damping does not lead to a significant improvement in the results.
机译:本文提出了一种基于优化的框架来确定存在的非线性模型的类型并识别其参数。该优化问题中的目的是通过最小化非线性结构的测量坐标处的实验和分析响应之间的差异来识别非线性模型的参数。在经过非线性电磁力的夹紧光束上证明了该方法的应用。在该方法中,假设是非线性力的形式是未知的。因此,可以假设可以使用泰勒序列扩展来描述任何非线性力。在本文中,假设四种不同的非线性形式模拟电磁力。这四种非线性模型的参数由由一系列阶梯式正弦振动测试获得的实验数据识别,该阶梯式振动试验具有恒定的加速度基础激励。结果发现,由线性阻尼和线性,二次,立方和第五阶刚度组成的非线性模型在预测的响应和相应的测量响应之间提供了出色的一致性。还表明,添加二次非线性阻尼不会导致结果的显着改善。

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