Experimental Identification of a Self-Sensing Magnetorheological Damper Using Soft Computing
Publication: Journal of Engineering Mechanics
Volume 141, Issue 7
Abstract
This paper presents the development and application of a soft-computing technique in identification of forward and inverse dynamics of a self-sensing magnetorheological (MR) damper based on experimental measurements. This technique is developed by the synthesis of an NARX (nonlinear autoregressive with exogenous inputs) model structure and neural network within a Bayesian inference framework. The Bayesian inference procedures essentially eschew overfitting that could occur in network learning and improve generalization (prediction) capability by regularizing the complexity of learning. In applying the developed technique to the self-sensing MR damper, the present and past information of its input and output quantities, which contain the physical knowledge of the damper, is used to formulate its nonlinear dynamics. The NARX network architecture is then optimized to enhance modeling effectiveness, efficiency, and robustness. Experimental data of the damper subjected to both harmonic and random excitations are used for model identification and assessment. Assessment results show that the formulated Bayesian NARX network accurately emulates the nonlinear forward dynamics of the self-sensing MR damper. Improved generalization (prediction) capability of the NARX network model by the Bayesian regulation is observed by comparing the modeling results with and without considering regularization. An inverse dynamic model for the self-sensing MR damper is further formulated by the developed technique. The proposed soft-computing technique is viable to formulate dynamic models of the self-sensing MR damper for structural control applications.
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Acknowledgments
The work described in this paper was supported in part by a grant from Hong Kong Polytechnic University (Project No. G-YJ94) and partially by a grant from the Innovation and Technology Support Programme of the Hong Kong Special Administrative Region, China (Project No. ITS/241/11).
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Published In
Journal of Engineering Mechanics
Volume 141 • Issue 7 • July 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Aug 21, 2013
Accepted: Jan 14, 2015
Published online: Apr 13, 2015
Published in print: Jul 1, 2015
Discussion open until: Sep 13, 2015
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