Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16425
Title: Characterization and modeling of a self-sensing MR damper under harmonic loading
Authors: Chen, ZH
Ni, YQ 
Or, SW 
Keywords: Bayesian regularization
Dynamic modeling
Hysteresis
NARX neural network
Piezoelectric force sensor
Self-sensing magnetorheological (MR) damper
Issue Date: 2015
Publisher: Techno Press
Source: Smart structures and systems, 2015, v. 15, no. 4, p. 1103-1120 How to cite?
Journal: Smart structures and systems 
Abstract: A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.
URI: http://hdl.handle.net/10397/16425
ISSN: 1738-1584
EISSN: 1738-1991
DOI: 10.12989/sss.2015.15.4.1103
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