Experimental evaluation of observers for tire–road forces, sideslip angle and wheel cornering stiffness

Baffet, Guillaume; Charara, Ali; Lechner, Daniël; Thomas, Daniel Brian

doi:10.1080/00423110701493963

Cited by 87 publications

(34 citation statements)

References 13 publications

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“…The FWVM is widely used to describe transversal vehicle dynamic behavior [3], [4], [6]. In this study, we adopt the following simplified assumptions:…”

Section: Vehicle-road Model a Four-wheel Vehicle Modelmentioning

confidence: 99%

“…We note that the torque is difficult to get in practice; it requires expensive sensors. More recently, in [5] and [6], authors propose observers to estimate lateral forces per axle without using torque measures. In [7], the authors propose an estimation process based on a three DOF vehicle model, as a tire force estimator.…”

Section: Introductionmentioning

confidence: 99%

“…In [8] and [9], sideslip angle estimation is discussed in details. In [5], [6], [7], lateral forces are modelled with a derivative equal to random noise. The authors in [7], remark that such modeling leads to a noticeable inaccuracy when estimating individual lateral tire forces, but not in axle lateral forces.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering

Doumiati

Victorino²,

Charara³

et al. 2009

2009 European Control Conference (ECC)

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Abstract-Extensive research has shown that most of road accidents occur as a result of driver errors. A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents. Preventing such kind of accidents using vehicle control systems, requires certain input data concerning vehicle dynamic parameters and vehicle road interaction. Unfortunately, some parameters like tire-road forces and sideslip angle, which have a major impact on vehicle dynamics, are difficult to measure in a car. Therefore, this data must be estimated. Due to the system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with cheap, easily-available standard sensors. Performances are tested and compared to real experimental data acquired using the INRETS-MA Laboratory car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral-tire forces and sideslip angle.

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“…The FWVM is widely used to describe transversal vehicle dynamic behavior [3], [4], [6]. In this study, we adopt the following simplified assumptions:…”

Section: Vehicle-road Model a Four-wheel Vehicle Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering

Doumiati

Victorino²,

Charara³

et al. 2009

2009 European Control Conference (ECC)

View full text Add to dashboard Cite

show abstract

“…Therefore, an online estimation method is necessary to be proposed in the self-driving control. In the past, Wenzel et al 2 and Baffet et al 3 proposed to use the dual extended Kalman filter (DEKF) for vehicle state estimation and vehicle parameter estimation, which made use of two Kalman filters (KFs) running in parallel. Best et al 4 considered a real-time state estimation of vehicle handling dynamics by the extended adaptive KF.…”

Section: Introductionmentioning

confidence: 99%

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Huang

Duan

Taheri

et al. 2015

Advances in Mechanical Engineering

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Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

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“…In [4] and [5], the sideslip estimator has been widely discussed. More recently, in [6], the authors estimate the lateral force by train and not at each wheel. Other studies, develop observers tire-road efforts, but they use an information on the wheels torque requiring high-cost sensors and difficult implementation.…”

mentioning

confidence: 99%

Motorcycle lateral dynamic estimation and lateral tire-road forces reconstruction using sliding mode observer

Slimi

Arioui

Mammar

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Extensive researches have shown that most of road accidents occur as a result of driver errors. A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents. This observation is even more valid on motorcycle, recall that, last decade about 1600 french people are killed each year [7]. Preventing such kind of accidents using vehicle control systems, requires certain input data concerning vehicle dynamic parameters and vehicle road interaction. Unfortunately, some parameters like tire-road forces and lateral speed, which have a major impact on vehicle dynamics, are difficult to measure. Therefore, this data must be estimated. Due to the system nonlinearities and unmodeled dynamics, an observer based on sliding technique is proposed in this paper. The estimation process method use the motorcycle dynamic response's and standard sensors cheap and easily-available. Observer performances are tested and compared to the motorcycle model responses in different simulation conditions, various initial conditions and measurement noise. The effectiveness of the present approach is make out through the simulation results and show its practical potential as a low-cost solution for calculating lateral-tire forces and motorcycle lateral speed in a accuarate way.

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Experimental evaluation of observers for tire–road forces, sideslip angle and wheel cornering stiffness

Cited by 87 publications

References 13 publications

Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering

Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Motorcycle lateral dynamic estimation and lateral tire-road forces reconstruction using sliding mode observer

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