Development and Experimental Evaluation of a Slip Angle Estimator for Vehicle Stability Control

Piyabongkarn, D.; Rajamani, Rajesh; Grogg, John; Lew, J.Y.

doi:10.1109/tcst.2008.922503

Cited by 292 publications

(60 citation statements)

References 13 publications

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“…A single track (or bicycle) model with two degrees of freedom is considered here to describe the vehicle behavior, according to (Rajamani, 2006;Piyabongkarn et al, 2009). Among the possible equivalent choices for the model state variables, we consider the body sideslip angle β and the yaw rate r since they are of particular interest when describing the vehicle lateral dynamics, which, under the assumption of negligible road bank angle, can be described by the following two equations (see, e.g., Rajamani, 2006)…”

Section: Brief Review Of Vehicle Lateral Dynamicsmentioning

confidence: 99%

“…These estimators, sometimes called virtual sensors, can be profitably used to replace physical sensors which cannot be mounted on production cars due to their prohibitive costs. In (Piyabongkarn et al, 2009) the authors discuss an effective algorithm for sideslip angle estimation based on a proper combination of the estimates provided by a linear single track model and a kinematics model. The problem of sideslip angle estimation is also considered in (Stephant et al, 2004) where a linear estimator based on the single track model is compared with three different nonlinear filters designed on the basis of some proper nonlinear extensions of the bicycle model.…”

Section: Introductionmentioning

confidence: 99%

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Set-membership LPV model identification of vehicle lateral dynamics

2011

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Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output LPV models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of manoeuvres is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameters measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed.

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Section: Brief Review Of Vehicle Lateral Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Set-membership LPV model identification of vehicle lateral dynamics

2011

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“…This threshold is chosen as 80 % (classical value used in the literature) in order to define a safety margin. Thanks to the fundamental principle of the dynamic applied to the roll model depicted Fig.1(b), and still assuming that the angles are small and moreover thatθ <<φ, dynamics equation for the roll angle ϕ and for the normal forces are respectively given by (5), (6) and (7):…”

Section: Roll Motion and Llt Computationmentioning

confidence: 99%

“…In the same way, in order to improve the vehicle stability, algorithms which estimate the sliding parameters (slip angle, tire cornering stiffnesses) and roll angle (preponderant variables for the risk of rollover) have been designed ( [5], [6] and [7]). These algorithms use linear tire-ground models, which are well adapted since grip conditions do not exhibit large variations.…”

Section: Introductionmentioning

confidence: 99%

On-line estimation of a stability metric including grip conditions and slope: Application to rollover prevention for All-Terrain Vehicles

Richier

Lenain

Thuilot

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Rollover is the principal cause of serious accidents for All-Terrain Vehicles (ATV), especially for light vehicles (e.g. quad bikes). In order to reduce this risk, the development of active devices, contributes a promising solution. With this aim, this paper proposes an algorithm allowing to predict the rollover risk, by means of an on-line estimation of a stability criterion. Among several rollover indicators, the Lateral Load Transfer (LLT) has been chosen because its estimation needs only low cost sensing equipment compared to the price of a light ATV. An adapted backstepping observer associated to a bicycle model is first developed, allowing the estimation of the grip conditions. In addition, the lateral slope is estimated thanks to a classical Kalman filter relying on measured acceleration and roll rate. Then, an expression of the LLT is derived from a roll model taking into account the grip conditions and the slope. Finally, the LLT value is anticipated by means of a prediction algorithm. The capabilities of this system are investigated thanks to full scale experiments with a quad bike.

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“…Finally, in [8,9] it has been proposed an approximating off-axle kinematics by an on-axle system with a similar steady response, in which model differences are regarded as perturbations for the path tracker, while classical fundamental problems of motion control for articulated vehicles have been presented. Many researchers have proposed a variety of articles, focusing only on the measurement, modeling and estimation of slip angles and the way that the external environment and dynamic models are affecting them [10,11], or examining the overall problem of kinematic modeling of a multi-body vehicle (multiple trailers), with articulated joints [12].…”

Section: Introductionmentioning

confidence: 99%

A full error dynamics switching modeling and control scheme for an articulated vehicle

Nayl

Nikolakopoulos

Gustafsson

2015

Int. J. Control Autom. Syst.

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In this article, a complete analysis towards the development of a switching modeling and control framework for an articulated vehicle, under the effect of varying slip angles will be presented. The established nonlinear kinematic model, of the nonholonomic articulated vehicle, will be transformed into an error dynamics model, which in the sequel will be linearized around multiple nominal slip angle cases. The proposed control architecture will consist of a switching control scheme, based on multiple model predictive controllers, for the articulated vehicle under varying slip angles. The controllers will be developed in order to improve the performance of the articulated vehicle's path tracking, while compensating the varying slippage effect. The current measured slip angle is being considered as the switching rule and a corresponding switching control scheme is being defined, being able to apply constraints on the states, the control signal and the output variables. Both the non-slip and slip models will be derived to highlight the significance of accounting for slips in path following control and their significant effect on deteriorating the performance of the overall control scheme when not considered. Multiple simulation results will be presented to prove the efficacy of the overall suggested scheme.

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Development and Experimental Evaluation of a Slip Angle Estimator for Vehicle Stability Control

Cited by 292 publications

References 13 publications

Set-membership LPV model identification of vehicle lateral dynamics

Set-membership LPV model identification of vehicle lateral dynamics

On-line estimation of a stability metric including grip conditions and slope: Application to rollover prevention for All-Terrain Vehicles

A full error dynamics switching modeling and control scheme for an articulated vehicle

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