Heavy vehicle suspension parameters identification and estimation of vertical forces: experimental results

Imine, Hocine; Madani, T.

doi:10.1080/00207179.2014.951882

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…The estimation results were used to estimate the tyre vertical forces under each wheel on a four-poster rig. The tyre vertical force estimation results are compared with force measurements of the four-poster rig's actuators [16]. Doumiati, Victorino [17] used a linear Kalman filter to estimate the road profile.…”

Section: Existing Approaches In the Literaturementioning

confidence: 99%

Vehicle suspension force and road profile prediction on undulating roads

Hamersma

Schalk

2020

Vehicle System Dynamics

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The aim of the proposed estimator and predictor is to predict the effect a change in suspension settings will have on the vehicle's dynamic response before the change in suspension settings is made. The efficacy of advanced driver assist systems (ADAS) such as ABS, traction control, torque vectoring and others deteriorate significantly on undulating roads [1,2]. By changing or modulating the suspension settings, the performance of these systems may be improved. The control system thus needs to make a decision regarding spring and damper settings, and the proposed estimator and predictor is capable of giving information to the controller before it makes its control decision. BackgroundThe use of controllable suspensions is prevalent throughout the vehicle industry. Semiactive suspensions are used on a wide array of vehicles, from heavy vehicle applications, such as military, mining and agricultural vehicles to high performance passenger vehicles and Sports Utility Vehicles (SUVs). The advantage of using a controllable suspension on a

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Section: Existing Approaches In the Literaturementioning

confidence: 99%

Vehicle suspension force and road profile prediction on undulating roads

Hamersma

Schalk

2020

Vehicle System Dynamics

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“…In Ref. [4], Imine and Madani developed a method for a heavy vehicle based on the sliding mode observer coupled with a Lagrangian formulation of the dynamical problem, whereas in Ref. [5], Hong et al developed a method based on a Dual Unscented Kalman Filter for a four-wheel vehicle model.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Identification of Vehicle Suspension Parameters: A Methodology Based on Synthetic Data Analysis

de Hoyos Fernández de Córdova,

Olazagoitia,

Gijón-Rivera

2024

Mathematics

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In this study, we introduce an innovative approach for the identification of vehicle suspension parameters, employing a methodology that utilizes synthetic and experimental data for non-invasive analysis. Central to our approach is the application of a basic local optimization algorithm, chosen to establish a baseline for parameter identification in increasingly complex vehicle models, ranging from quarter-vehicle to half-vehicle (bicycle) models. This methodology enables the accurate simulation of the vehicle dynamics and the identification of suspension parameters under various conditions, including road perturbations such as speed bumps and curbs, as well as in the presence of noise. A significant aspect of our work is the ability to process real-world data, making it applicable in practical scenarios where data are obtained from onboard sensor equipment. The methodology was developed in MatLab, ensuring portability across platforms that support this software. Furthermore, the study explores the application of this methodology as a tool for denoising, enhancing its utility in real-world data analysis and predictive maintenance. The findings of this research provide valuable insights for vehicle suspension design, offering a cost-effective and efficient solution for dynamic parameter identification without the need for physical disassembly.

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“…T p p P P P T P P P P P ; and 2 3 2 3 2 3 2 4 2 2 2 2 2 2 4 2 2 2 2 2 2 ( ) ( )( ) ( ) [ , ] T Z is a function of the pitch displacement 3 x and velocity 4 x , which can be measured using commercial transducers [14,36]. Hence, similar to Theorem 1, the convergence of pitch motion control (27) can be proved, which is not repeated again.…”

Section: B Adaptive Ppf Control For Pitch Motionmentioning

confidence: 99%

“…In this case, some commercial sensors (e.g. accelerometer or laser sensor [14,36]) can be used to provide the required measurements for the NN and feedback control. Sensor noise can be taken as a part of the disturbance 1 d .…”

Section: A Adaptive Ppf Control For Vertical Displacementmentioning

confidence: 99%