A unified MR damper model and its inverse characteristics investigation based on the neuro-fuzzy technique

Ying, He; Liang, Guanqun; Bing, Xue; Zhi-zhao, Peng; Wei, Yintao

doi:10.3233/jae-180114

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…In 2019, He et al [121] proposed a mechanism-based unifed MR-damper model based on a neuro-fuzzy technique to represent the direct dynamics of MR dampers. Based on kinetics and rheology, the damping force of an MR damper consists of the friction, elasticity, viscosity, inertia, and shear terms.…”

Section: General Dynamic Modelsmentioning

confidence: 99%

Magnetorheological Fluid Dampers: A Close Look at Efficient Parametric Models

Bahar,

Pozo,

Meybodi

et al. 2024

Structural Control and Health Monitoring

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The reasonable efficiency, high reliability, and minimal maintenance requirements of the semiactive control algorithms make this form of structural control the most successful method from a practical point of view. In addition, significant progress has been made in structural control devices. Magnetorheological (MR) dampers have been the subject of extensive research. In order to develop and implement a successful structural control algorithm, a deep understanding of their operation is required. Therefore, many efforts have been made to classify MR dampers as efficient tools; however, their complicated behavior has also been noted. Various relations have been proposed to describe the highly nonlinear behavior of MR dampers. To develop an efficient control algorithm, one must first properly understand the seismic behavior of the structure and the control device as well as its handling. The handling of the device will be efficient if there is a description for its control, a so-called inverse model. This model is an answer to the question of whether the proposed device model can be written in a form that practically brings the generated damping force as close as possible to the desired force. In the identification process, the selection of a basic mathematical model is critical. In this review article, we have compiled successful parametric models that accurately replicate the behavior of MR dampers. Since the primary functionality of the device is crucial for the development of a competent control algorithm, we have endeavored to investigate and present the reversibility of the proposed models. We also delved at the characteristics of the parameters that enable precise communication between the central control unit and the device. These parameters, which enable the dialog between the processing unit and the control devices, will play an important role in the development of the inverse model in its simplicity and efficiency.

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Section: General Dynamic Modelsmentioning

confidence: 99%

Magnetorheological Fluid Dampers: A Close Look at Efficient Parametric Models

Bahar,

Pozo,

Meybodi

et al. 2024

Structural Control and Health Monitoring

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“…To accurately represent the dynamic characteristics of MR damper, a lot of researchers have proposed a number of damper dynamic models (He et al, 2019; Liu et al, 2020). In Pang et al (2018), a non-parametric model based on adaptive neuro-fuzzy inference system (ANFIS) was proposed to accurately simulate the characteristics of MR damper with fewer parameters to be identified.…”

Section: Modeling Of Vehicle Sasmentioning

confidence: 99%

On an enhanced back propagation neural network control of vehicle semi-active suspension with a magnetorheological damper

Wang

Pang

Luo

et al. 2022

Transactions of the Institute of Measurement and Control

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To improve the ride quality of a vehicle, an enhanced vibration control method is presented for semi-active suspension (SAS) with magnetorheological (MR) damper by combining back propagation neural network (BPNN) and particle swarm optimization (PSO). Based on the test data of MR damper, a non-parametric model of MR damper using adaptive neuro-fuzzy inference system (ANFIS) is first established, and based on that, a dynamics model of the SAS system is derived. Next, a BPNN controller is designed to fulfill the effective control of the current in MR damper. Meanwhile, the improved PSO with adaptive weight and dynamic acceleration constant is introduced to optimize the weights and thresholds of the BPNN controller, which can avoid the designed BPNN falling into the local optimum and then improve the convergence rate of the designed controller. Besides, the stability of the developed controller is analyzed via Lyapunov stability theory. Different from the existing models and methods, the established model can well describe the dynamics behaviors of the actual MR damper, and the proposed control method has better adaptability, convergence speed and precision. Finally, a simulative investigation is performed to validate the effectiveness and feasibility of the proposed controller, compared to existing BP-PID control and the passive suspension, the vehicle acceleration of SAS with this proposed controller is respectively improved by 10% and 30%.

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“…Schurter and Roschke (2000) presented an alternative about modeling a damper in the form of a Takagi-Sugeno-Kang fuzzy inference system, namely ANFIS (adaptive neuro-fuzzy inference system). Founded on the ANFIS, He et al (2019b) established an inverse model to achieve much more tracking capability for the desired damping force. Nugroho et al (2013) considered the implementation of an ANFIS technique to control a quarter vehicle suspension system with a semi-active MR damper.…”

Section: Introductionmentioning

confidence: 99%

A grey hysteresis model of magnetorheological damper

Deng

Dong

et al. 2021

Journal of Intelligent Material Systems and Structures

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Owing to the complex nonlinear hysteresis of magnetorheological (MR) damper, the modeling of an MR damper is an issue. This paper examines a novel MR damper hysteresis model based on the grey theory, which can fully mine the internal laws for the data with small samples and poor information. To validate the model, the experiment is conducted in the MTS platform, and then the experimental results are compiled to identify the model parameters. Considering the complexity of the grey model and its inverse model solution, the grey model is simplified in two ways based on the grey relational analysis method. Furthermore, the simplified grey model compares to other models to prove the superiority of the grey model. The analysis suggests the fitting results correspond to the measured results, and the mean relative error (MRE) of grey model is within 2.04%. After the grey model is simplified, its accuracy is slightly reduced, while its inverse model is easier to solve and makes a unique solution. Finally, compared with the polynomial and Bouc-Wen model, the novel model with fewer identification parameters has high accuracy and predictive ability. This novel model has fabulous potential in designing the control strategy of MR damper.

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A unified MR damper model and its inverse characteristics investigation based on the neuro-fuzzy technique

Cited by 11 publications

References 37 publications

Magnetorheological Fluid Dampers: A Close Look at Efficient Parametric Models

Magnetorheological Fluid Dampers: A Close Look at Efficient Parametric Models

On an enhanced back propagation neural network control of vehicle semi-active suspension with a magnetorheological damper

A grey hysteresis model of magnetorheological damper

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