A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

Duchanoy, Carlos A.; Moreno-Armendáriz, Marco A.; Moreno-Torres, Juan C; Cruz-Villar, Carlos A.

doi:10.3390/s19061333

Cited by 22 publications

(14 citation statements)

References 52 publications

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“…The reason may be: In Ref. [12], (1) the number of hidden layers is set to a fixed value, which is 3, and (1) RBFNN algorithm can be used to model the regulating mechanism of HAD, which has a much higher accuracy when compared with MLP and FNN.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the use of controllable suspension systems (i.e., SAS and AS) is an inevitable choice for high-performance vehicles [7]- [12]. Compared with the active type, the spring stiffness and damping coefficient of SAS are adjusted according to the optimized parameters of springs and shock absorbers under various conditions stored inside the computer, resulting in low energy consumption, reliable performance, and low cost [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

“…Hyperparameter optimization is a time-consuming task. To reduce the computational cost, in the existing HAD researches [6] [12], only a few of hyperparameters have been optimized, which may lead to the failure of obtaining a high-precision model. To solve this problem, an adaptive modeling method based on hyperparameter optimization algorithm is presented in this work.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, an adaptive modeling method based on hyperparameter optimization algorithm is presented in this work. The "adaptive" refers to automatic selection, relative to manual [6] and semi-adaptive [12] methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

Lin

Huang

et al. 2020

IEEE Access

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Artificial neural network surrogate models are often used in the design optimization of the automotive semi-active suspension system. To realize the desired damping force, a surrogate model needs to be constructed to approximate the regulating mechanism of the hydraulic adjustable damper. However, very few of the existing studies discuss how to guarantee the modeling accuracy. To this end, this work constructs a novel surrogate model by using radial basis function neural network. Meanwhile, an adaptive modeling method based on modified hyperband and trust-region-based mode pursuing sampling is presented. Concretely, modified hyperband is used to fast select a seed model by early-stopping and dynamic resource allocation. Mode pursuing sampling is then performed in the neighborhood of the seed model, to systematically generate more sample points while statistically covering the entire neighborhood (i.e., trust region). In particular, in the mode pursuing sampling procedure, quadratic regression is performed around the current optimum as the second detection. Moreover, as the position or size of the trust region changes, the sampling and detection process iterate until the accuracy of the model is no longer improved. To avoid falling into the local optimum, the seed model selection and mode pursuing sampling process iterate until the termination criterion is met. The experimental results show that compared with the benchmarks, the modeling accuracy of hydraulic adjustable damper can be improved by up to 48%, and the iteration resources can be reduced by up to 84%. INDEX TERMS semi-active suspension systems, hydraulic adjustable damper, artificial neural network, hyperparameter optimization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

Lin

Huang

et al. 2020

IEEE Access

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“…TDNNs were first introduced in [38]. The following works represent the results of the successful implementation of time delay neural networks in the design of magnetorheological damper controllers [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Active Shock Absorber Control Based on Time-Delay Neural Network

2020

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A controlled suspension usually consists of a high-level and a low-level controller. The purpose the high-level controller is to analyze external data on vehicle conditions and make decisions on the required value of the force on the shock absorber rod, while the purpose of the low-level controller is to ensure the implementation of the desired force value by controlling the actuators. Many works have focused on the design of high-level controllers of active suspensions, in which it is considered that the shock absorber can instantly and absolutely accurately implement a given control input. However, active shock absorbers are complex systems that have hysteresis. In addition, electro-pneumatic and hydraulic elements are often used in the design, which have a long response time and often low accuracy. The application of methods of control theory in such systems is often difficult due to the complexity of constructing their mathematical models. In this article, the authors propose an effective low-level controller for an active shock absorber based on a time-delay neural network. Neural networks in this case show good learning ability. The low-level controller is implemented in a simplified suspension model and the simulation results are presented for a number of typical cases.Energies 2020, 13, 1091 2 of 16 of a semi-active shock absorber lies in I and III quadrants; thus, the force vector F is always in one direction with the velocity vector v.Energies 2020, 13, 1091 2 of 15In an active shock absorber, the set of dissipative characteristics cover all IV quadrants due to an external energy source, and force and velocity vectors can be oppositely directed. This property is the great advantage of active shock absorbers, and it allows, in theory, the achievement of a complete absence of movement of the vehicle body for the given planes.

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Using Machine Learning to Predict the Seismic Response of an SDOF RC Structure with Superelastic Dampers

Shao

Andrawes

2022

Int J Civ Eng

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A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

Cited by 22 publications

References 52 publications

Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

Adaptive Artificial Neural Network Surrogate Model of Nonlinear Hydraulic Adjustable Damper for Automotive Semi-Active Suspension System

Active Shock Absorber Control Based on Time-Delay Neural Network

Using Machine Learning to Predict the Seismic Response of an SDOF RC Structure with Superelastic Dampers

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