A novel inverse dynamic model for a magnetorheological damper based on network inversion

Boada, Mjl; Boada, María Jesús López; Díaz, V.

doi:10.1177/1077546317705991

Cited by 24 publications

(13 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly used nonlinear black-box modeling method is neural network. 35,36 In comparison with the nonlinear control method, the inverse model method is based on the constitutive relation between the desired control force and the controllable parameter; thus, this method does not need rich experience, and the tracking accuracy of the desired control force is high. According to structure and working principle of the air spring with auxiliary chamber, the stiffness and damping have strong nonlinearity, the relationship between the damping force and the area of the variable throttle orifice and the piston speed is complicated, so the analytical inverse model of the air spring is difficult to establish.…”

Section: Inverse Model Of Air Springmentioning

confidence: 99%

“…Using the experimental or simulated data of the desired control force and controllable parameter, the nonanalytic inverse modeling method establishes a nonanalytical model between the two variables by nonlinear black‐box modeling to achieve the mapping from the desired control force to the controllable parameter. The most commonly used nonlinear black‐box modeling method is neural network 35,36 . In comparison with the nonlinear control method, the inverse model method is based on the constitutive relation between the desired control force and the controllable parameter; thus, this method does not need rich experience, and the tracking accuracy of the desired control force is high.…”

Section: Semi‐active Control Strategy Of Air Suspensionmentioning

confidence: 99%

See 1 more Smart Citation

Semi‐active control of air suspension with auxiliary chamber subject to parameter uncertainties and time‐delay

Zhang

Wang

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Suspension systems are critical to the ride comfort and handling stability of vehicles, but traditional passive suspensions fail to achieve optimal performance in the two aspects. Semi-active suspension controller changes the suspension stiffness or damping to improve the ride comfort and handling stability, which has the potential advantages of low energy consumption and high reliability. In this paper, the model of an air spring with an auxiliary chamber and a variable throttle orifice is proposed and the semi-active control strategy of air suspension is carried out. The parameters of the air spring with auxiliary chamber are optimized to improve the performance when the control system fails and to increase the basic performance for semi-active control. Based on the linear matrix inequality approach to robust H ∞ control, the semi-active control strategy of the air suspension is studied, in which parameter uncertainties and time delay are considered to improve control robustness. An inverse model of the air spring is then established to calculate the area of the variable throttle orifice, by which the desired control force is tracked accurately. Finally, the effectiveness of the semi-active control system of the air suspension is verified by numerical simulation. Comparing with the passive suspension, not only under nominal parameters, but also with parameter uncertainties, time delay, and both parameter uncertainties and time delay, the semi-active control system proposed in this paper has good control performance as well as is strong robustness to parameter uncertainties and time delay. K E Y W O R D S air suspension, parameter uncertainties, robust control, semi-active control, time delay 1 INTRODUCTION Comfort and safety are important considerations in the automotive industry, 1,2 and the ride comfort and handling stability are direct manifestations of these two properties. Suspension systems are used to transfer forces and moments between the car body and the ground. Thus, the suspension design must be improved to enhance the ride comfort and handling stability of vehicles. However, traditional passive suspensions fail to achieve optimal performance in both ride comfort and handling stability. 3,4 In recent years, in-wheel-motored electric vehicles have become a research hotspot because of their compact structure, high transmission efficiency, and 7130

show abstract

Section: Inverse Model Of Air Springmentioning

confidence: 99%

Section: Semi‐active Control Strategy Of Air Suspensionmentioning

confidence: 99%

Semi‐active control of air suspension with auxiliary chamber subject to parameter uncertainties and time‐delay

Zhang

Wang

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…2) The parameters of the model are learned online and updated in real-time. MJL Boada et al proposed a new inverse model for MR dampers based on network inversion [42]. However, this research did not update the model itself, only updated other input variables in the inversion process, and the proposed forward model could not capture the damping force behavior close to the peak values in low frequency excitations.…”

Section: Introductionmentioning

confidence: 99%

A New High-Precision General Inverse Model Based on Deep Learning for Magnetorheological Damper

2021

View full text Add to dashboard Cite

There are several main factors affect damping characteristics of a magnetorheological damper, the influence laws of some ones are eager to change with varying working conditions. Therefore, it is challengable to establish the relationship between the two: influence factors and damping characteristics of the magnetorheological damper. This paper proposed a new deep learning-based general inverse model to predict the required current value of a magnetorheological damper under changing complex working conditions. Specifically, a fully-connected multilayer perceptron was chosen to train the general forward model. The complex and time-varying relationships between the main factors and damping characteristics were accurately characterized on use of the high nonlinear mapping ability of the multilayer perceptron. Compared to most of general forward models, the proposed model was more excellent with an accuracy of 99.73% based on ensuring generality. Besides, the network inversion method was used in the inverse solution of the multilayer perceptron general forward model, which simplified the process of inverse solution without requiring a mass of training data. Furthermore, a genetic algorithm was used to replace the gradient descent method which was commonly used in network inversion, with the benefit of solving the problem of selecting the initial value and step size, as well as improving the accuracy. Simultaneously, the experiments were conducted on the proposed general inverse model, the experimental results, being consistent with the simulation results, demonstrated that the proposed model can accurately predict the control current value and track the required damping force in changing complex working conditions.INDEX TERMS General forward model, general inverse model, genetic algorithm, multilayer perceptron, network inversion.

show abstract

“…To overcome this problem, many works on MR dampers suggest using the isolator dynamical inverse model in the control system design phase (Boada et al, 2018;Nguyen et al, 2017aNguyen et al, , 2017b. Specifically, the aim was to find the existing relationship between the magnetization current and the actuator force for generating a proper control command to drive the MRE-based isolator.…”

Section: Introductionmentioning

confidence: 99%

A combined neural network and model predictive control approach for ball transfer unit–magnetorheological elastomer–based vibration isolation of lightweight structures

Brancati

Massa

Pagano

et al. 2020

Journal of Vibration and Control

View full text Add to dashboard Cite

This study addresses the possibility of adopting semi-active magnetorheological elastomers–based isolators for protecting lightweight structures from ground vibration. The exploitation of these smart devices has the main advantage of controlling their stiffness and damping features by acting on the magnetic field generated by a coil on the basis of the actual conditions of both the lightweight structure and the surrounding environment. This allows for combining the reliability of passive devices with the benefits of active control methods. Both mechanical and control system designs could play a crucial role in the challenging problem of improving isolation performances. To solve this issue, we (i) suggest a novel ball transfer unit–magnetorheological elastomer–based isolation system prototype to obtain an improved isolation response of the lightweight structure with respect to the exclusive use of an magnetorheological elastomer and (ii) propose a novel robust combined neural network and model-predictive control approach, allowing proper functioning of the ball transfer unit–magnetorheological elastomer–based isolation system. The effectiveness of the proposed semi-active isolator in guaranteeing vibrational isolation of lightweight structures is evaluated by considering a rack cabinet composed of three storeys and subject to an El Centro earthquake. Numerical simulations confirm and disclose the efficacy of the proposed approach.

show abstract

A novel inverse dynamic model for a magnetorheological damper based on network inversion

Cited by 24 publications

References 42 publications

Semi‐active control of air suspension with auxiliary chamber subject to parameter uncertainties and time‐delay

Semi‐active control of air suspension with auxiliary chamber subject to parameter uncertainties and time‐delay

A New High-Precision General Inverse Model Based on Deep Learning for Magnetorheological Damper

A combined neural network and model predictive control approach for ball transfer unit–magnetorheological elastomer–based vibration isolation of lightweight structures

Contact Info

Product

Resources

About