Modeling of a magneto-rheological damper: An improved multi-state-dependent parameter estimation approach

Zamani, Abbas-Ali; Tavakoli, Saeed; Etedali, Sadegh; Sadeghi, Jafar

doi:10.1177/1045389x19835938

Cited by 16 publications

(10 citation statements)

References 47 publications

(94 reference statements)

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“…To evaluate the potential of MR dampers in structural control applications, a model must be developed to describe the behavior of the MR damper accurately. Both parametric and non-parametric models have been proposed to describe the behavior of MR dampers (Bui et al, 2021; Wang and Liao, 2011; Xu et al, 2021; Yang et al, 2013; Zamani et al, 2019). Parametric models based on mechanical idealizations have been studied by several researchers (Yang et al, 2021; Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical study of the forward and inverse models of an MR gel damper using a GA-optimized neural network

Gong

Tan

Xiong

et al. 2023

Journal of Intelligent Material Systems and Structures

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In this paper, we present a series of experimental and numerical studies on the performance and modeling of a developed magnetorheological gel (MRG) damper. A bi-directional shear-type damper was designed and fabricated. The MRG damper, which utilizes the gel’s high viscosity, can effectively alleviate the settlement problem inherent in magnetorheological fluid damper applications. Then, dynamic performance experiments were carried out to obtain the damping force with sinusoidal and random displacement excitations. Based on the test results, the forward model of the damper was established using a backpropagation neural network. A genetic algorithm was employed to optimize both the network structure parameters and the initial weight and bias values. Different forward models generated using different training datasets were validated and compared with the RBFNN model and Bouc-Wen model using different test datasets. The validation results indicate that the neural network-based forward model greatly outperforms the RBFNN model and Bouc-Wen model in terms of the estimation performance. The influence of the inputs at previous time has also been investigated. Finally, to generate the command current for controlling the damper, inverse neural network models with optimized structure parameters were established using different training datasets. Validation results with different test datasets indicate that, although the predicted current generated by the inverse models has many high-frequency components, it can still act as an effective damper controller, with the resulting damping force calculated using the predicted current coinciding well with the desired behavior.

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Section: Introductionmentioning

confidence: 99%

Experimental and numerical study of the forward and inverse models of an MR gel damper using a GA-optimized neural network

Gong

Tan

Xiong

et al. 2023

Journal of Intelligent Material Systems and Structures

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“…However, the study of the nonparametric model mainly relies on the input and output data of the MR damper under different conditions in the test process for analysis and training. For example, Zamani et al proposed an MSDP model, which obtained force-displacement and force-velocity data from different excitation amplitude, frequency, and current values for parameter identification of the model and achieved high accuracy [17]. Although the nonparametric model has high precision, it has a high requirement on the sample data volume, and its training processes, such as fuzzy logic determination, neural network, and fuzzy neural network processes, are quite complicated.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Parameter Identification of the MR Damper Based on LS-SVM

Qian

Xiao-liang

Ouyang

2021

International Journal of Aerospace Engineering

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In order to identify the nonlinear characteristics of the magnetorheological (MR) damper applied in multi-DOF vibration reduction platforms in the aerospace field in the modeling process, the least square support vector machine (LS-SVM) method is adopted, because LS-SVM can handle small-sample, high-dimensional characteristic problems. Firstly, the theory of the modeling method based on LS-SVM was illustrated including the genetic algorithm (GA) optimization method. Secondly, the characteristic curve of the MR damper was tested based on different conditions. Then, the current and historical input displacement, velocity, and current and the historical output are taken as the input of the LS-SVM model and the damping force of the current output is taken as the output of the model for model training. Meanwhile, the genetic algorithm is introduced to optimize the parameters of the LS-SVM model which affect the accuracy of the model, the penalty factor c = 16.48 , and the kernel parameter σ = 3.39 after optimization. Finally, in order to verify the method adopted in the paper, the Simulink model was simulated in certain input conditions; by comparing the simulation and experimental values of this model, it is found that the maximum error is within 10 N and the average error is around 0.89 N, which is similar to the accuracy obtained in other works of literature, and the correctness of this model is verified.

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“…Entretanto a modelagem de amortecedores magnetoreológicosé um problema desafiador devido ao seu comportamento histerético Zamani et al (2019). Neste trabalho, o objetivoé determinar o comportamento dinâmico do amortecedor MR, ou seja,é considerado a dinâmica nãolinear (histerese) inerente do fluido MR, Stutz (2005).…”

Section: Introductionunclassified

Plataforma de ensaios dinˆamicos com amortecedor magneto-reol´ogico: identifica¸c˜ao em modelo de histerese

Raminelli

Lévano

Montezuma

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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O amortecedor magneto-reol´ogico ´e um dispositivo que possui um fluido interno capaz de ser controlado atrav´es da corrente el´etrica. A corrente el´etrica controla a resistˆencia do dispositivo, um recurso que permite aos usu´arios controlar a rigidez do amortecedor contra movimentos. Este artigo apresenta um procedimento de identifica¸c˜ao para um amortecedor magneto-reol´ogico. A identifica¸c˜ao foi realizada com dados experimentais: os dados indicam queo modelo Dahl mostra resultados promissores.

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Modeling of a magneto-rheological damper: An improved multi-state-dependent parameter estimation approach

Cited by 16 publications

References 47 publications

Experimental and numerical study of the forward and inverse models of an MR gel damper using a GA-optimized neural network

Experimental and numerical study of the forward and inverse models of an MR gel damper using a GA-optimized neural network

Modeling and Parameter Identification of the MR Damper Based on LS-SVM

Plataforma de ensaios dinˆamicos com amortecedor magneto-reol´ogico: identifica¸c˜ao em modelo de histerese

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