Evaluation of a sliding mode observer for vehicle sideslip angle

Stéphant, Joanny; Charara, Ali; Meizel, Dominique

doi:10.1016/j.conengprac.2006.04.002

Cited by 123 publications

(76 citation statements)

References 5 publications

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“…[9][10][11][12][13][14][15][16][17][18] When the vehicle manoeuvres well below the limits of tyre adhesion, observers based around linear vehicle models with linear tyre behaviour are sufficient. [9,12] Adaptive or nonlinear tyre models must be adopted if the observer is to function when the limits of tyre adhesion are approached (e.g.…”

Section: Introductionmentioning

confidence: 99%

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

Morrison

Cebon

2016

Vehicle System Dynamics

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Various active safety systems proposed for articulated heavy goods vehicles (HGVs) require an accurate estimate of vehicle sideslip angle. However in contrast to passenger cars, there has been minimal published research on sideslip estimation for articulated HGVs. Stateof-the-art observers, which rely on linear vehicle models, perform poorly when manoeuvring near the limits of tyre adhesion. This paper investigates three nonlinear Kalman filters (KFs) for estimating the tractor sideslip angle of a tractor-semitrailer. These are compared to the current state-of-the-art, through computer simulations and vehicle test data. An unscented KF using a 5 degrees-of-freedom single-track vehicle model with linear adaptive tyres is found to substantially outperform the state-of-the-art linear KF across a range of test manoeuvres on different surfaces, both at constant speed and during emergency braking. Robustness of the observer to parameter uncertainty is also demonstrated. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

Morrison

Cebon

2016

Vehicle System Dynamics

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“…Nakhaeinejad et al [4] suggested a similar approach not only for analysis of the faults, but also for defining a guideline for model-based sensor selection for induction motors. Among all of the abovementioned methods, the condition number is more common and is frequently used in the literature [1], [5], [6]. In this paper, all of the introduced observability and sensor configuration methods are chosen and applied to determine the best sensor configuration for estimation of vehicle states.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Configuration and Fault-Tolerant Estimation of Vehicle States

Zarringhalam

Rezaeian

Fallah

et al. 2013

SAE Int. J. Passeng. Cars – Electron. Electr. Syst.

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This paper discusses observability of the vehicle states using different sensor configurations as well as fault-tolerant estimation of these states. The optimality of the sensor configurations is assessed through different observability measures and by using a 3-DOF linear vehicle model that incorporates yaw, roll and lateral motions of the vehicle. The most optimal sensor configuration is adopted and an observer is designed to estimate the states of the vehicle handling dynamics. Robustness of the observer against sensor failure is investigated. A fault-tolerant adaptive estimation algorithm is developed to mitigate any possible faults arising from the sensor failures. Effectiveness of the proposed fault-tolerant estimation scheme is demonstrated through numerical analysis and CarSim simulation.

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“…In [7], the authors propose an estimation process based on a three DOF vehicle model, as a tire force estimator. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Evaluation of a sliding mode observer for vehicle sideslip angle

Cited by 123 publications

References 5 publications

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

Optimal Sensor Configuration and Fault-Tolerant Estimation of Vehicle States

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

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