NDLI: Study on control strategy of magneto rheological semi-active suspension with neural network inverse model

Content Provider	IEEE Xplore Digital Library
Author	Wu Jian Liu Zhiyuan
Copyright Year	2014
Description	Author affiliation: Dept. of control Sci. & Eng., Harbin Inst. of Technol., Harbin, China (Wu Jian; Liu Zhiyuan)
Abstract	In this paper, a neural network inverse model of magneto rheological (MR) damper is established and combined with a new force control algorithm to achieve the controlling of vehicle semi-active suspension. Combined with the Skyhook algorithm and ADD (Acceleration-Driven-Damper) algorithm, this paper presents a new damping force control algorithm which can improve the high frequency damping characteristics and is easy to combine with the current damper mechanical model. In order to realize the transforming from damping force to drive current, we further analyze the characteristics of the modified Bouc-Wen, polynomial and many different models, and combined with the test data, we get a hyperbolic model which can better reflect the MR damper dynamic. In order to facilitate the real-time calculation, a neural network inverse model is established based on the hyperbolic model. Finally, the improvement of the control method on suspension comfort performance is validated through the 1/4 suspension and full vehicle simulation.
Starting Page	257
Ending Page	262
File Size	963884
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896631
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	semi-active suspension Magnetomechanical effects Neural networks magneto rheological damper Shock absorbers Electronic mail Mathematical model MATLAB neural network
Content Type	Text
Resource Type	Article

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