Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations
| Autor: | Maren Scheel, Jean-Philippe Noël, Gleb Kleyman, Simon Peter, Ali Tatar, Malte Krack, Matthew S. Allen, Matthew R. W. Brake |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
0209 industrial biotechnology Polynomial Computer science Modal testing Aerospace Engineering Basis function Systems and Control (eess.SY) 02 engineering and technology 01 natural sciences Resonance (particle physics) Electrical Engineering and Systems Science - Systems and Control 020901 industrial engineering & automation Control theory 0103 physical sciences FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing 010301 acoustics Civil and Structural Engineering Nonlinear system identification Mechanical Engineering System identification Computer Science Applications Vibration Nonlinear system Control and Systems Engineering Signal Processing |
| Popis: | 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 of both methods and 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 the capabilities and limitations of the models. 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. The final version of this article is available online at http://doi.org/10.1016/j.ymssp.2020.106796 |
| Databáze: | OpenAIRE |
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