NDLI: Modeling of Magneto-rheological Fluid Damper Employing Recurrent Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Changrong Liao Keli Wang Miao Yu Weimin Chen
Copyright Year	2005
Description	Author affiliation: Center for Intelligent Structures, Chongqing Univ. (Changrong Liao; Keli Wang; Miao Yu)
Abstract	Due to inherent nonlinear behaviors of magneto-rheological (MR) fluid dampers, one of challenges for utilizing effectively these devices as actuators to control vibration of mechanical system is to develop accurate models. A recurrent neural networks, with 3 input neurons and 1 output neuron in input layer and out layer respectively and 7 recurrent neurons in the hidden layer, is used to simulate behaviors of automotive MR fluid damper to develop control algorithms for suspension systems. The recursive prediction error algorithms are applied to train the recurrent neural networks using test data from lab where the MR fluid dampers were tested by the MTS electro-hydraulic servo vibrator system. Training of recurrent neural networks has been done by means of recursive prediction error algorithms presented in this paper and data generated from test above-mentioned. In comparison with experimental results of MR fluid damper, the recurrent neural networks are reasonably accurate to depict performances of MR fluid damper over a wide range of operating conditions
Starting Page	616
Ending Page	620
File Size	2226229
Page Count	5
File Format	PDF
ISBN	0780394224
DOI	10.1109/ICNNB.2005.1614709
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Damping Vibrations System testing Actuators Recurrent neural networks Neurons Magnetic devices Nonlinear control systems Prediction algorithms Shock absorbers
Content Type	Text
Resource Type	Article

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