Application of adaptable functional series vector time-dependent autoregressive model for extraction of real modal parameters for identification of time-varying systems
Autor: | Viet Hung Vu, Zhaoheng Liu, Wenchao Li, Marc Thomas, Bruce Hazel |
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Rok vydání: | 2017 |
Předmět: |
0209 industrial biotechnology
Engineering Series (mathematics) business.industry Applied Mathematics Control engineering 02 engineering and technology Condensed Matter Physics 01 natural sciences Operational Modal Analysis Identification (information) Matrix (mathematics) 020901 industrial engineering & automation Modal Autoregressive model 0103 physical sciences Robot State (computer science) Electrical and Electronic Engineering business 010301 acoustics Instrumentation Algorithm |
Zdroj: | Measurement. 103:143-156 |
ISSN: | 0263-2241 |
DOI: | 10.1016/j.measurement.2017.02.027 |
Popis: | This paper presents a method for extraction of real modal parameters for identification of time-varying systems using adaptable functional series vector time-dependent autoregressive (AFS-VTAR) model. In the practical application of AFS-VTAR model, both real modal parameters and computational ones may mix together in the identified results. In order to extract the real ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in AFS-VTAR is proposed. The efficiency of the proposed method is successfully demonstrated first through numerical simulations applied to a time-varying 3-degree-of-freedom system and then experimentally by applying it to a flexible robot in motion. |
Databáze: | OpenAIRE |
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