2013
DOI: 10.1088/0964-1726/22/10/105016
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Direct voltage control of magnetorheological damper for vehicle suspensions

Abstract: The paper presents a study on the direct voltage control of a magnetorheological (MR) damper for application in vehicle suspensions. As MR damper dynamics is highly nonlinear, the direct control system design for an MR damper is difficult. Representing an MR damper by a Takagi–Sugeno (TS) fuzzy model enables the linear control theory to be directly applied to design the MR damper controller. In this paper, first the MR damper dynamics is represented by a TS fuzzy model, and then an H∞ controller that considers… Show more

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Cited by 64 publications
(37 citation statements)
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“…Two excitation signals were used to evaluate the whole system: bump road profile and random road profile. These two excitations are generated by the equation (21) and equation (22), which are classic road profile generation method [31][32]. The expression for the bump excitation is:…”
Section: Effectiveness Evaluation and Analysis Of The Damper On A mentioning
confidence: 99%
“…Two excitation signals were used to evaluate the whole system: bump road profile and random road profile. These two excitations are generated by the equation (21) and equation (22), which are classic road profile generation method [31][32]. The expression for the bump excitation is:…”
Section: Effectiveness Evaluation and Analysis Of The Damper On A mentioning
confidence: 99%
“…This section describes the design of the H  controller and the stability analysis of fuzzy systems based on a T-S state space model. With the application of the parallel distributed compensation scheme [23], the observer-based controller for the nonlinear system can be designed as…”
Section: H∞ Controller Designmentioning
confidence: 99%
“…Considering that the friction has similar hysteresis phenomenon as MR damper, the inner friction is modelled into MR damper force together. The seat suspension model is defined as: (Yang et al, 2013;Du et al, 2013). The optimized values of those parameters are determined by fitting the model with experimental data using MATLAB parameter estimation toolbox.…”
Section: Model Identificationmentioning
confidence: 99%