Data-driven prediction-control system modeling for magnetorheological damping force

Lv, Hongzhan; Sun, Qi; Ma, Weiguang

doi:10.1177/1045389x221103784

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…It indicated that the bi-exponential model reduces about 10% more than the Bouc-Wen model by the hardware in the loop simulation test. Lv et al [25] proposed a decision tree classification algorithm to reversely control the desired current based on the nonlinear autoregressive with external input neural network. Compared with the traditional inverse model, the prediction accuracy is improved by nearly 20%.…”

Section: Introductionmentioning

confidence: 99%

A forward-inverse dynamic model for the hydraulic damping(magnetorheological) actuator based on cyclic stress–strain

Ma,

Huang,

Huang

et al. 2024

Smart Mater. Struct.

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Magnetorheological dampers (MRDs) are applied to hydraulic systems, which not only improve the underdamped characteristics of valve-controlled cylinder systems, but also help hydraulic actuators to resist high load impact. However, the high power density leads to the complexity of the internal flow channel of the damper, which seriously affects the output accuracy of the damping force. It can lead to the fact that existing dynamics models cannot accurately describe the hysteresis characteristics of the MRD. Therefore, this study proposes a simple and general dynamic model of MRD, which solves the problem that existing models are complex and difficult to invert. Firstly, the hydraulic damping actuator (HDA) with the series MRD is taken as the research object. Based on the stress-strain hysteresis characteristics under the cyclic constitutive model, the hyperbolic tangent curve is reorganized and normalized. It can accurately describe the yield formation and yield dissipation stages of the hysteresis loop. Secondly, the relationship between the parameters of the dynamic model and the current is obtained according to the mechanical experimental data. Then the inverse model of the MRD is established by using the method of section-backstepping. Finally, in the static experiment, the Mean Absolute Percentage Error(MAPE) of the force at different velocity is less than 7.5%; In the dynamic experimental test, the MAPE of the force is 9.7%. The inverse dynamics model is verified to have high tracking performance under both static and dynamic forces. And it also indirectly confirms the effectiveness of the forward model.

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Section: Introductionmentioning

confidence: 99%

A forward-inverse dynamic model for the hydraulic damping(magnetorheological) actuator based on cyclic stress–strain

Ma,

Huang,

Huang

et al. 2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…As a result, Saharuddin et al [43] proposed a modelling approach based on Extreme Learning Machines (ELM), which avoids gradient-based backpropagation for weight adjustments, and instead employs the Moore-Penrose generalized inverse, to overcome limitations of existing ANN models. Lv et al [44] used the nonlinear autoregressive with external input neural network to predict the MRD output damping force, realizing accurate characterization of nonlinear details of MRD damping characteristics. Han et al [45] employed the fully-connected multilayer perceptron (MLP) to train the general forward model, achieving characterizing the nonlinear features of MRD damping characteristics taking advantage of MLP's strong nonlinear mapping capability.…”

Section: Introductionmentioning

confidence: 99%

Multi-condition adaptive detail characterization model of magnetorheological dampers and experimental verification

Lei,

Li,

et al. 2024

Phys. Scr.

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The theoretical model for predicting the damping characteristics of magnetorheological dampers (MRDs) is significant for enhancing the design efficiency of the control algorithm. However, some existing theoretical models face limitations in characterizing MRD damping characteristics simultaneously in terms of nonlinear detail characterization and adaptability to variable working conditions. Therefore, this paper proposed the Composite Double-Boltzmann (CDB) model combining the Double-Boltzmann (DB) function widely used in the field of biology and chemistry for its strong nonlinear characterization capability. Utilizing this model to fit the sinusoidal vibration testing data of the MRD prototype under variable combination working conditions, obtaining quantitative relationships between the undetermined parameters in the CDB model and the excitation current, vibration frequency, and amplitude to enable the model to address both the nonlinear details characterization of MRDs and adaptability to variable working conditions. Subsequently, the validity of the quantitative relationships were verified by comparing the calculated parameter values using the quantitative relationships with the original accurate parameter values. In order to verify the validity of the CDB model, extensive unknown working condition vibration tests were conducted on the MRD prototype under variable excitation currents, vibration frequencies, amplitudes and random excitation working conditions, employing the CDB and Tanh models to predict the damping characteristics, to compare to demonstrate the CDB model’s capability of adapting to variable working conditions while accurately characterizing the nonlinear details of MRD damping characteristics.

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“…Ebrahim et al [50] proposed a general evolutionary algorithm based on the Kwok model to model MRDs. Liu et al [51] and Lv et al [52] adopted a nonlinear auto-regressive model with exogenous inputs neural network for modeling MRDs. Gong et al [53] adopted a genetic algorithm optimization neural network for modeling magnetorheological gel dampers (similar to MRDs).…”

Section: Introductionmentioning

confidence: 99%

Modeling of magnetorheological dampers based on a dual-flow neural network with efficient channel attention

Li,

Luo,

Zhang

et al. 2023

Smart Mater. Struct.

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Magnetorheological dampers (MRDs) are intelligent devices for semi-active control and are widely applied in vibration isolation. A high-fidelity modeling method is necessary to take full advantage of the controllable properties of MRDs. Therefore, a nested long short-term memory (NLSTM)-convolutional neural network-efficient channel attention (NLCE) modeling method based on a dual-flow neural network architecture is proposed herein. It uses the time, current, amplitude, frequency, displacement, and velocity as inputs and the damping force as the output. Extensive sinusoidal excitation experiments were conducted using a materials test system and two datasets (large and small sample numbers) were obtained. Five testing sets with different emphases were obtained from different experimental series. Four evaluation indexes were used for a quantitative comparison. First, after training with the large sample dataset, network ablation and comparison experiments were conducted based on a testing set-1. The mean absolute relative error (MARE) evaluation index decreased by 2.290% relative to that of the NLSTM (baseline), indicating that the NLCE method is optimal for predicting the motion characteristics of MRDs. Furthermore, after training with the small sample dataset, comparison experiments were conducted based on testing set-1 and testing set-2. The MAREs decreased by 3.984% and 0.871% relative to that of the NLSTM (baseline), respectively, indicating that the NLCE is also the best modelling method for small sample dataset. The visualization results from the above experiments verified the abilities of the NLCE modeling method for small sample-adaptation, fighting randomness, and identifying similarities. Finally, based on testing set-3, testing set-4 and testing set-5, the NLCE model trained with small sample datasets has high prediction accuracy in predicting the peak damping force (MAREs = 1.456%, 0.880%, and 1.482%, respectively), indicating a high prediction accuracy in the non-hysteretic region. Combining all of the experimental results shows that the NLCE is an effective method for predicting the motion characteristics of MRDs.

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Data-driven prediction-control system modeling for magnetorheological damping force

Cited by 5 publications

References 42 publications

A forward-inverse dynamic model for the hydraulic damping(magnetorheological) actuator based on cyclic stress–strain

A forward-inverse dynamic model for the hydraulic damping(magnetorheological) actuator based on cyclic stress–strain

Multi-condition adaptive detail characterization model of magnetorheological dampers and experimental verification

Modeling of magnetorheological dampers based on a dual-flow neural network with efficient channel attention

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