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Background <p>The vehicle model's Magneto-Rheological (MRD) damper parameters are selected to match the experimental&#xa0;damper's properties and correlate to a real-world damper.</p> Purpose <p>This study main characteristic is its upgrading of the MRD's earthquake-present limitations while considering soil structure impacts. The equivalent linearization method is used iteratively to produce a nonlinear vehicle model's control and response statistics that uses an MR damper.</p> Methods <p>The results are verified using the Black Widow with Ant Lion Algorithm (BWALO), and Deep Neural Network (DNN) computation is used to determine the optimal limits of MRD. A performance index, which is a group of vehicle performance parameters such as mass acceleration, displacement, and the rigidity of the front and rear suspensions, is decreased by the best control using preview. To have computationally capable models to concentrate on the qualities of the soil-structure system is achieved through limited part diversions in which soil-structure interaction is represented by remarkable impedance capacities.</p> Results <p>The findings demonstrate that the developed BWALO algorithm can identify the MR dampers' ideal parameters. In addition to helping researchers better understand earthquake vibration, this study helps fashion designers attain higher MRD for all designs.</p>

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Vibration Analysis of MR Damper in Non-Linear Suspension System by Hybrid optimization with Deep learning

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • S. Tennison Augustine Jebaraj,
  • N. Ramasamy,
  • M. Dev Anand,
  • N. Santhi

摘要

Background

The vehicle model's Magneto-Rheological (MRD) damper parameters are selected to match the experimental damper's properties and correlate to a real-world damper.

Purpose

This study main characteristic is its upgrading of the MRD's earthquake-present limitations while considering soil structure impacts. The equivalent linearization method is used iteratively to produce a nonlinear vehicle model's control and response statistics that uses an MR damper.

Methods

The results are verified using the Black Widow with Ant Lion Algorithm (BWALO), and Deep Neural Network (DNN) computation is used to determine the optimal limits of MRD. A performance index, which is a group of vehicle performance parameters such as mass acceleration, displacement, and the rigidity of the front and rear suspensions, is decreased by the best control using preview. To have computationally capable models to concentrate on the qualities of the soil-structure system is achieved through limited part diversions in which soil-structure interaction is represented by remarkable impedance capacities.

Results

The findings demonstrate that the developed BWALO algorithm can identify the MR dampers' ideal parameters. In addition to helping researchers better understand earthquake vibration, this study helps fashion designers attain higher MRD for all designs.