Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations
Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations
复制标题
近共振多项式非线性状态空间和非线性模式模型的实验评估
DOI：
10.1016/j.ymssp.2020.106796
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Krack
中科院分区：
文献类型：
--
作者：
M. Scheel;Gleb Kleyman;A. Tatar;M. Brake;S. Peter;J. Noël;M. Allen;M. Krack
在本文中，使用两种现有的非线性系统识别方法来识别数据驱动模型。第一种方法着重于使用稳态激励来识别系统。为了实现这一目标，实现了锁相环控制器来获取谐振附近的周期振荡并构建非线性模式模型。该模型基于振幅相关的模态特性，即不需要非线性基函数。第二种方法利用宽带随机输入的无控制实验，利用先进的系统识别工具建立多项式非线性状态空间模型。将该方法应用于两个实验试验台，一个是磁性悬臂梁，另一个是带搭接的自由-自由梁。然后，对两种试样分别采用两种方法获得的模型进行挑战，以预测在不同正弦和正弦扫描激励下观察到的动态、近共振行为。非线性模态和状态空间模型的振动预测明显地突出了其能力和局限性。通过设计，非线性模式模型在共振峰处产生完美匹配，在近距离处产生高精度。然而，它仅限于良好的间隔模式和正弦激励。状态空间模型涵盖了更广泛的动态范围，包括瞬态激励。然而，本研究中考虑的实际非线性只能用多项式基函数来近似。因此，识别的状态空间模型被发现是高度依赖输入的，特别是对于正弦激励，它们被发现导致低预测能力。
In the present paper, two existing nonlinear system identification methodologies are used to identify data-driven models. The first methodology focuses on identifying the system using steady-state excitations. To accomplish this, a phase-locked loop controller is implemented to acquire periodic oscillations near resonance and construct a nonlinear-mode model. This model is based on amplitude-dependent modal properties, i.e. does not require nonlinear basis functions. The second methodology exploits uncontrolled experiments with broadband random inputs to build polynomial nonlinear state-space models using advanced system identification tools. The methods are applied to two experimental test rigs, a magnetic cantilever beam and a free-free beam with a lap joint. The respective models obtained by either method for both specimens are then challenged to predict dynamic, near-resonant behavior observed under different sine and sine-sweep excitations. The vibration prediction of the nonlinear-mode and state-space models clearly highlight capabilities and limitations. The nonlinear-mode model, by design, yields a perfect match at resonance peaks and high accuracy in close vicinity. However, it is limited to well-spaced modes and sinusoidal excitation. The state-space model covers a wider dynamic range, including transient excitations. However, the real-life nonlinearities considered in this study can only be approximated by polynomial basis functions. Consequently, the identified state-space models are found to be highly input-dependent, in particular for sinusoidal excitations where they are found to lead to a low predictive capability.
影响因子：
4.7
作者：
L. Renson;A. Gonzalez-Buelga;D. Barton;S. Neild
通讯作者：
L. Renson;A. Gonzalez-Buelga;D. Barton;S. Neild
影响因子：
2.8
作者：
Barton, David A. W.;Mann, Brian P.;Burrow, Stephen G.
通讯作者：
Burrow, Stephen G.
影响因子：
8.4
作者：
Balaji, Nidish Narayanaa;Chen, Wei;Brake, Matthew R. W.
通讯作者：
Brake, Matthew R. W.