&lt;title&gt;Performance of long-stroke and low-stroke MR fluid dampers&lt;/title&gt;

Boelter, Ralf; Janocha, Hartmut

doi:10.1117/12.310693

Cited by 11 publications

(14 citation statements)

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“…Although the discovery of both ER and MR fluids dates back to the late 1940's, researchers have primarily concentrated on ER fluids for civil engineering applications [5][6][7][8]. Recently developed MR fluids appear to be an attractive alternative to ER fluids for use in controllable fluid dampers [9][10][11][12]. MR fluids are smart materials, which typically consist of micron-sized, magnetically polarizable particles dispersed in a carrier medium such as mineral or silicone oil.…”

Section: Introductionmentioning

confidence: 99%

Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism

2009

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A magneto-rheological (MR) fluid damper is a semi-active control device that has recently begun to receive more attention in the vibration control community. However, the inherent nonlinear nature of the MR fluid damper makes it challenging to use this device to achieve high damping control system performance. The development of an accurate modeling method for a MR fluid damper is necessary because of its unique characteristics. Our goal was to develop an alternative method for modeling an MR fluid damper by using a self tuning fuzzy (STF) method based on neural technique. The behavior of the researched damper is directly estimated through a fuzzy mapping system. To improve the accuracy of the STF model, a back propagation and a gradient descent method are used to train online the fuzzy parameters to minimize the model error function. A series of simulations were done to validate the effectiveness of the suggested modeling method when compared with the data measured from experiments on a test rig with a researched MR fluid damper. Finally, modeling results show that the proposed STF interference system trained online by using neural technique could describe well the behavior of the MR fluid damper without need of calculation time for generating the model parameters.

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Section: Introductionmentioning

confidence: 99%

Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism

2009

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“…Although the discovery of both ER and MR fluids dates back to the late 1940's, researchers have primarily concentrated on ER fluids for civil engineering applications ( [5][6][7][8]). Recently developed MR fluids appear to be an attractive alternative to ER fluids for use in controllable fluid dampers [9][10][11][12][13][14][15]. The initial discovery and development of MR fluid can be credited to Jacob Rainbow at the US National Bureau of Standards in the late 1940s [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…The initial discovery and development of MR fluid can be credited to Jacob Rainbow at the US National Bureau of Standards in the late 1940s [9,10]. These fluids are suspensions of micron-sized, magnetizable particles in an appropriate carrier liquid [11][12][13][14][15]. Normally, MR fluids are free flowing liquids having consistency similar to that of motor oil.…”

Section: Introductionmentioning

confidence: 99%

MR Fluid Damper and Its Application to Force Sensorless Damping Control System

Truong¹,

Ahn²

2012

Smart Actuation and Sensing Systems - Recent Advances and Future Challenges

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“…Earlier versions of semi-active suspension systems were regulated by adjusting the resistance to fluid flow through oil orifices, which is too slow for the speed of the mechanical motion [21]. Latter applications, however, use electrorheological and magnetorheological fluids, which are smart materials made by mixing fine particles into a low viscosity liquid [4]. To study the performance of semi-active suspension systems, several models are developed and reported in the literature [16].…”

Section: Semi-active Suspension Systemmentioning

confidence: 99%

Solution Approaches to Differential Equations of Mechanical System Dynamics: A Case Study of Car Suspension System

Terefe¹,

Lemu²

2018

Adv. Sci. Technol. Res. J.

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Solution of a dynamic system is commonly demanding when analytical approaches are used. In order to solve numerically, describing the motion dynamics using differential equations is becoming indispensable. In this article, Newton's second law of motion is used to derive the equation of motion the governing equation of the dynamic system. A quarter model of the suspension system of a car is used as a case and sinusoidal road profile input was considered for modeling. The state space representation was used to reduce the second order differential equation of the dynamic system of suspension model to the first order differential equation. Among the available numerical methods to solve differential equations, Euler method has been employed and the differential equation is coded MATLAB. The numerical result of the second degree of freedom, quarter suspension system demonstrated that the approach of using numerical solution to a differential equation of dynamic system is suitable to easily simulate and visualize the system performance.

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<title>Performance of long-stroke and low-stroke MR fluid dampers</title>

Cited by 11 publications

References 0 publications

Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism

Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism

MR Fluid Damper and Its Application to Force Sensorless Damping Control System

Solution Approaches to Differential Equations of Mechanical System Dynamics: A Case Study of Car Suspension System

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