Developing Military Light Utility Vehicle Performance Based on Air Semi-Active Suspension System Using Recurrent Neural Network–Based Controller

<div>This research presents a semi-active suspension system that combines an air spring and a magneto-rheological (MR) fluid damper to produce both active force and variable damping rates based on the road conditions. The suspension system used for the military light utility vehicle (MLUV) has seven degrees of freedom. A nonlinear model predictive control system generates the desired active force for the air spring control signal, while the linear quadratic regulator (LQR) estimates the target tracking of the intended damping force. The recurrent neural network is designed to develop a controller for an identification system. To achieve the optimal voltage for the MR damper without log time, it is used to simultaneously determine the active control force of the air spring by modifying the necessary damping force tracking. The MLUV suspension system is integrated with the traction control system to improve overall vehicle stability. A fuzzy traction controller adjusts the throttle angle based on the driver’s throttle input and the slip ratio of the driving wheels. Constant speed, passing maneuvers, increasing acceleration, and forceful braking are the four scenarios the driver uses to assess the traction control capability. Investigations are conducted to examine the interaction between the suspension and traction systems and how this interaction influences the integrated model that represents the vehicle’s behavior and performance. The effectiveness of the suspension is assessed under bump and random road excitations, based on the presentation of vehicle performance criteria in both the time and frequency domains. The results of the simulation show that in terms of ride comfort and vehicle stability, the air–MR suspension system performs significantly better than the passive suspension system. A fuzzy traction controller can smooth out the torque applied to the vehicle’s wheels by adjusting the engine’s speed and torque.</div>

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Publication Details

Journal
SAE International journal of vehicle dynamics, stability, and NVH
Published
2025-06-19
DOI
https://doi.org/10.4271/10-09-04-0030
Citations
1
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.68
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article

Developing Military Light Utility Vehicle Performance Based on Air Semi-Active Suspension System Using Recurrent Neural Network–Based Controller

Ahmed Shehata Gad
1 citations
SAE International journal of vehicle dynamics, stability, and NVH
Vehicle Dynamics and Control Systems
0.68
article

Developing Military Light Utility Vehicle Performance Based on Air Semi-Active Suspension System Using Recurrent Neural Network–Based Controller

Ahmed Shehata Gad
article en
1 citations

Abstract

<div>This research presents a semi-active suspension system that combines an air spring and a magneto-rheological (MR) fluid damper to produce both active force and variable damping rates based on the road conditions. The suspension system used for the military light utility vehicle (MLUV) has seven degrees of freedom. A nonlinear model predictive control system generates the desired active force for the air spring control signal, while the linear quadratic regulator (LQR) estimates the target tracking of the intended damping force. The recurrent neural network is designed to develop a controller for an identification system. To achieve the optimal voltage for the MR damper without log time, it is used to simultaneously determine the active control force of the air spring by modifying the necessary damping force tracking. The MLUV suspension system is integrated with the traction control system to improve overall vehicle stability. A fuzzy traction controller adjusts the throttle angle based on the driver’s throttle input and the slip ratio of the driving wheels. Constant speed, passing maneuvers, increasing acceleration, and forceful braking are the four scenarios the driver uses to assess the traction control capability. Investigations are conducted to examine the interaction between the suspension and traction systems and how this interaction influences the integrated model that represents the vehicle’s behavior and performance. The effectiveness of the suspension is assessed under bump and random road excitations, based on the presentation of vehicle performance criteria in both the time and frequency domains. The results of the simulation show that in terms of ride comfort and vehicle stability, the air–MR suspension system performs significantly better than the passive suspension system. A fuzzy traction controller can smooth out the torque applied to the vehicle’s wheels by adjusting the engine’s speed and torque.</div>

SAE International journal of vehicle dynamics, stability, and NVHVol. 09(4)
Helwan University (EG)
Affordable and clean energy
Openalex Percentile: Top 29%
Vehicle Dynamics and Control Systems
0.68
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Developing Military Light Utility Vehicle Performance Based on Air Semi-Active Suspension System Using Recurrent Neural Network–Based Controller — Ahmed Shehata Gad · SAE International journal of vehicle dynamics, stability, and NVH (2025) | TGRS Research Map | TGRS