The experimental identification of magnetorheological dampers and evaluation of their controllers

Metered, H.; Bonello, Philip; Oyadiji, S. Olutunde

doi:10.1016/j.ymssp.2009.09.005

Cited by 96 publications

(68 citation statements)

References 18 publications

(23 reference statements)

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“…It is a powerful network that is widely used in many applications. The RNN model of MR damper which was proposed by Metered et al 10 has two kinds of external inputs; such as previously applied currents and past displacements, and uses the estimated force from prior iterations as an internal input. Simulation results indicated that the network using only displacement cannot provide accurate results, so the RNN is modified in this paper by replacing the displacement input with velocity and acceleration.…”

Section: Recurrent Neural Network Setupmentioning

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%

“…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. RNN is an alternative model that does not require force sensors 10 . However, it is complex and requires more time to train.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Recurrent Neural Network Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 includes the treatment of structural control systems that use MR dampers. Therefore, research works focused on the control of structures dealt with the management of systems through various control algorithms based on mathematical models, fuzzy logic, genetic algorithms and neural networks [4,9,[21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Structural control strategies based on magnetorheological dampers managed using artificial neural networks and fuzzy logic

Lara¹,

Brito²,

Graciano³

2017

revuin

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Este artículo puede compartirse bajo la licencia CC BY-ND 4.0 y se referencia usando el siguiente formato: L. Lara, J. Brito, C. Graciano, "Structural control strategies based on magnetorheological dampers managed using artificial neural networks and fuzzy logic", UIS Ingenierías, vol. ABSTRACTThis paper presents a numerical assessment on the performance of two structural control strategies based on magnetorheological (MR) dampers. At first, a control strategy based on artificial neural networks was employed on a simple structure to control vibration. This controller combines a predictive model function to control forces and an inverse model of voltage calculation to manage the MR dampers. Secondly, a control strategy based on fuzzy logic was also used. Therefore, the controller governs the actions from a set of rules that represent the heuristics of the system to be controlled. Results achieved from the numerical simulations indicate that the performance of these two control strategies is promising and satisfactory, based on response reductions of up to 83% relative to the performance of the system without control. KEYWORDS:Control of structures, Vibration reduction, Magnetorheological dampers, Artificial neural networks, Fuzzy logic. RESUMENEn este trabajo se presenta una evaluación numérica sobre el desempeño de dos estrategias de control estructural basado en amortiguadores magnetoreológicos (MR). En primer lugar, se empleó una estrategia de control basada en redes neuronales artificiales en una estructura simple para el control de vibraciones. Este controlador combina una función de modelo predictivo para las fuerzas de control y un modelo inverso del cálculo de la tensión para manejar los amortiguadores MR. En segundo lugar, se utilizó una estrategia de control basada en lógica difusa. De esta forma, el controlador gobierna las acciones de un conjunto de reglas que representan la heurística del sistema a controlar. Los resultados de las simulaciones numéricas indican que el rendimiento de estas dos estrategias de control es prometedor y satisfactorio, basado en la reducción de la respuesta de hasta un 83% en relación con el rendimiento del sistema sin control.

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“…The preferred identification strategy for capturing nonlinearities and other complexities in bearings is nonparametric ("black-box") since it makes no assumption on the nature of the underlying relationship between input and output, unlike parametric identification [13]. Chebyshev polynomial interpolation [14][15][16] and neural networks (NNs) [13,17] fall in this category.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis

Hassan

Bonello

2016

Journal of Tribology

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to different input data sets; (ii) by using it in the harmonic balance (HB) solution process for the unbalance response of a rotor-bearing system. In either case, the results from the identified variable-speed RNN maintain very good correlation with the benchmark over a wide range of speeds, in contrast to an earlier identified constant-speed RNN, demonstrating the great potential of this method in the absence of selfexcitation effects.

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The experimental identification of magnetorheological dampers and evaluation of their controllers

Cited by 96 publications

References 18 publications

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

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

Structural control strategies based on magnetorheological dampers managed using artificial neural networks and fuzzy logic

A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis

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