Neural Network Modeling of a Magnetorheological Damper

Chang, Chin-Chen; Roschke, Paul N.

doi:10.1177/1045389x9800900908

Cited by 147 publications

(95 citation statements)

References 14 publications

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“…and Roschke [55] developed a neural network model to emulate the dynamic behavior of MR dampers. However, the non-parametric damper models are quite complicated.…”

Section: Dynamic Modeling Of Mr Dampersmentioning

confidence: 99%

Large-scale MR fluid dampers: modeling and dynamic performance considerations

Yang

Spencer

Carlson

et al. 2002

Engineering Structures

662

508

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The magnetorheological (MR) damper is one of the most promising new devices for structural vibration reduction. Because of its mechanical simplicity, high dynamic range, low power requirements, large force capacity and robustness, this device has been shown to mesh well with application demands and constraints to offer an attractive means of protecting civil infrastructure systems against severe earthquake and wind loading. In this paper, an overview of the essential features and advantages of MR materials and devices is given. This is followed by the derivation of a quasi-static axisymmetric model of MR dampers, which is then compared with both a simple parallel-plate model and experimental results. While useful for device design, it is found that these models are not sufficient to describe the dynamic behavior of MR dampers. Dynamic response time is an important characteristic for determining the performance of MR dampers in practical civil engineering applications. This paper also discusses issues affecting the dynamic performance of MR dampers, and a mechanical model based on the Bouc-Wen hysteresis model is developed. Approaches and algorithms to optimize the dynamic response are investigated, and experimental verification is provided.

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“…and Roschke [55] developed a neural network model to emulate the dynamic behavior of MR dampers. However, the non-parametric damper models are quite complicated.…”

Section: Dynamic Modeling Of Mr Dampersmentioning

confidence: 99%

Large-scale MR fluid dampers: modeling and dynamic performance considerations

Yang

Spencer

Carlson

et al. 2002

Engineering Structures

662

508

View full text Add to dashboard Cite

show abstract

“…EHM is used to represent hysteresis characteristics of the MR damper. Conventional FNN can estimate damping force accurately but requires force information from a previous state 9 . To do this, force sensors must be installed in the system, otherwise the damping force cannot be predicted accurately because it can only approximate one-to-one or multiple-to-one mapping, whereas hysteresis requires multi-valued mapping.…”

Section: Introductionmentioning

confidence: 99%

“…These parametric modelling methods require assumptions about the structure of the mechanical model, and accuracy can decrease if the initial assumptions about model structure are flawed, or if the proper constraints are not applied to the parameters 8 . Another type of MR damper model employs non-parametric approaches such as a feed-forward neural network (FNN) 9 , recurrent neural network (RNN) 10 , neurofuzzy 11 and black-block model 8 . Non-parametric models generally require more experimental data for training than parametric models.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to model magneto-rheological dampers using EHM with a feed-forward neural network

Ekkachai¹,

Tungpimolrut²,

Nilkhamhang³

2012

ScienceAsia

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This paper proposes a novel method for modelling magneto-rheological (MR) dampers. It uses an elementary hysteresis model (EHM) with a feed-forward neural network (FNN) to capture hysteresis characteristics of an MR damper, and another FNN to determine the current gain. These parts can be trained separately, thus reducing the size of the training dataset. The inputs of the proposed model include velocity, acceleration, and current to estimate the generated damping force. Unlike previous FNN models, this model does not require force sensor inputs. Simulation results show the high performance of the proposed EHM-based FNN when compared to conventional methods such as a recurrent neural network.

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“…They could also be more effectively used for plant modeling in practical control applications. Chang and Roschke [6] proposed a non-parametric model using multi-layer perception neural network integrated optimization method for a satisfactory representation of damper behavior. Wang and Liao [1,7] explored the modeling of MR dampers by using a trained direct identification based on recurrent neural network.…”

Section: Introductionmentioning

confidence: 99%

Non-Parametric Model of Magneto-Rheological (MR) Damper Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Zheng

Liu

Zhou

2011

AEF

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Abstract. Magneto-rheological (MR) damper is a typical non-linear actuator. The nonlinear relationship between the input and the output of MR damper should be characterized accurately in order to produce a required control action supplied by MR damper. The challenges of the conventional parametric model are the time-consuming and the requirement of complex parameter identification for MR damper. In this paper, a non-parametric model of MR damper based on the adaptive neuro-fuzzy system theory is presented to overcome the drawbacks of the conventional parametric model. This non-parametric model is developed by means of two adaptive neural fuzzy sub-systems which are designed to describe the nonlinear relationship in MR damper successfully. One sub-system is used to characterize the dependence of the damping force on velocity and acceleration, the other sub-system is used to characterize the dependence of the damping force level on the control voltage. The proposed non-parametric model is identified by experimental results. Furthermore, the accuracy of the model is investigated and evaluated. The model supplies a key technical support to achieve excellent control performances for semi-active suspension systems with MR dampers in vehicle.

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Neural Network Modeling of a Magnetorheological Damper

Cited by 147 publications

References 14 publications

Large-scale MR fluid dampers: modeling and dynamic performance considerations

Large-scale MR fluid dampers: modeling and dynamic performance considerations

A novel approach to model magneto-rheological dampers using EHM with a feed-forward neural network

Non-Parametric Model of Magneto-Rheological (MR) Damper Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

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