Paper
27 June 2002 Prediction of ER long-stroke damper response: model updating methods
Neil D. Sims, Roger Stanway, C. X. Wong
Author Affiliations +
Abstract
Smart fluid devices are now seen as an attractive solution to vibration damping problems. They offer superior performance compared to passive devices, without involving the cost, weight and complexity of fully active damping strategies. However, the inherent non-linearity of smart fluid dampers makes it difficult to fully exploit their capabilities, due the problems in applying an effective control strategy. In the past much of the research focused on complex controllers involving techniques such as neural networks and fuzzy logic. In recent years, however, an alternative approach has been adopted, whereby classical control techniques are used to linearise the damper's response. As a result some applications for smart fluid damping now use combinations of proportional, integral, or derivative control methods. However, it appears that these controllers can become unstable in much the same way as for a truly linear system. In order to investigate this instability it is suggested that a sufficiently accurate model of the damper's response is required, so that the onset of instability can be reproduced numerically. In this contribution, a model updating technique is described whereby an existing ER damper model is updated in line with experimental data. The paper begins with an overview of the experimental test facility and the modeling approach. The updating algorithm is then described, and it is shown how the updated model improves significantly on the accuracy of the model predictions.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Neil D. Sims, Roger Stanway, and C. X. Wong "Prediction of ER long-stroke damper response: model updating methods", Proc. SPIE 4697, Smart Structures and Materials 2002: Damping and Isolation, (27 June 2002); https://doi.org/10.1117/12.472666
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Cited by 1 scholarly publication.
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KEYWORDS
Data modeling

Fluid dynamics

Motion models

Instrument modeling

Control systems design

Electrodes

Mechanical engineering

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