Mixed Skyhook and FxLMS Control of a Half-Car Model with Magnetorheological Dampers

Piotr Krauze,Jerzy Kasprzyk

doi:10.1155/2016/7428616

Piotr Krauze, Jerzy Kasprzyk

Open Access

https://doi.org/10.1155/2016/7428616

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Abstract

The problem of vibration attenuation in a semiactive vehicle suspension is considered. The proposed solution is based on usage of the information about the road roughness coming from the sensor installed on the front axle of the vehicle. It does not need any preview sensor to measure the road roughness as other preview control strategies do. Here, the well-known Skyhook algorithm is used for control of the front magnetorheological (MR) damper. This algorithm is tuned to a quarter-car model of the front part of the vehicle. The rear MR damper is controlled by the FxLMS (Filtered-x LMS) taking advantage of the information about the motion of the front vehicle axle. The goal of this algorithm is to minimize pitch of the vehicle body. The strategy is applied for a four-degree-of-freedom (4-DOF) vehicle model equipped with magnetorheological dampers which were described using the Bouc-Wen model. The suspension model was subjected to the road-induced excitation in the form of a series of bumps within the frequency range 1.0–10 Hz. Different solutions are compared based on the transmissibility function and simulation results show the usefulness of the proposed solution.

Highlights

Damping of mechanical vibrations in vehicles using passive solution has a very long history and is widely used in practice
Inherent stability and low power consumption is favoured over active suspension force generators, so MR dampers are widely used in vehicle suspension systems [3,4,5]
The paper deals with vibration control related to the model of the experimental vehicle equipped with the automotive MR dampers

Summary

Introduction

Damping of mechanical vibrations in vehicles using passive solution has a very long history and is widely used in practice. The other approach, based on the FxLMS algorithm modified for semiactive devices, was tested in [16] for a quarter-car model and in [17] for a half-car model In all these references it is assumed that some kind of information about the road roughness is available in advance. In practice, the problem of the appropriate measurement of the road profile that can be available and useful for the control algorithm in real-time is very difficult It can be obtained using specialized vision systems or laser scanners aimed in front of the vehicle.

Modelling of Experimental Setup

Control Algorithm

Results

Conclusions