Sideslip angle estimation based on input–output linearisation with tire–road friction adaptation

Gao, Xiaojie; Yu, Zhuoping; Neubeck, Jens; Wiedemann, Jochen

doi:10.1080/00423110902721824

Cited by 54 publications

(31 citation statements)

References 14 publications

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“…There are several studies related with vehicle sideslip angle estimations in the literature [178][179][180][181][182][183][184][185][186][187][188][189][192][193][194]. In [179], a nonlinear tire model based observer (NTMBO) is developed for estimating the vehicle body slip angle.…”

Section: Sideslip Angle and Roll Angle Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

305

119

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Section: Sideslip Angle and Roll Angle Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

305

119

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“…A number of authors have criticized the computational efficiency of the EKF and have proposed alternative methods for the online estimation of the sideslip angle. In [8], authors propose an adaptive sideslip angle observer which is basically a high-gain observer (HGO) based on input-output linearization. Finally, in [9], authors propose to utilise an Unscented Kalman Filter (UKF) for vehicle state estimation purposes, precising that UKF is a numerically more efficient nonlinear stochastic estimation technique when compared to EKF, a fact that they prove through both simulations and experimental results.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear sideslip observer design methodology for automotive vehicles based on a rational tire model

Solmaz

Başlamışlı

2011

Int J Adv Manuf Technol

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In this paper we develop a nonlinear vehicle sideslip observer design that is based on a nonlinear lateral dynamics vehicle model. In doing so we utilize a novel simplified rational tire model to compute the lateral wheel forces. This tire model is significantly simpler than the well known Magic Formula (in terms of the number of model parameters), yet it provides sufficient detail over a wide range of operating conditions for the purpose of estimating the sideslip angle. The input to the nonlinear observer are typical signals that are available within lateral stability control systems, which include vehicle speed, steer angle, lateral acceleration and yaw rate measurements. In our analysis, we assume the road friction to be a known parameter. We utilize a recent theorem from the literature and show that the suggested nonlinear state estimator a) is asymptotically stable for the case where the observer makes use of the exact tire model, b) is stable (in the sense of Lyapunov) providing uniformly bounded error dynamics for the case where it makes use the proposed rational tire model to approximate the exact tire model. Finally, we provide numerical simulations to demonstrate the efficacy of our nonlinear observer based estimation technique under varying road friction conditions.

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“…Different models, such as linear [18] and nonlinear [19], and observers such as Kalman Filters [10,20] have also been considered in order to estimate the sideslip angle. A common feature of most of these observers for the estimation of the sideslip angle is that they rely heavily on an accurate tire model, which may vary during vehicle operation.…”

Section: Introductionmentioning

confidence: 99%

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Boada

Díaz

2016

Mechanical Systems and Signal Processing

123

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(2016). Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mechanical Systems and Signal Processing,[72][73] ABSTRACTMost existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in the relevant literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. In this paper, we propose a novel observer based on ANFIS, combined with Kalman Filters in order to estimate the sideslip angle, which in turn is used to control the vehicle dynamics and improve its behavior. For this reason, low-cost sensor measurements which are integrated into the actual vehicle and executed in real time have to be used. The ANFIS system estimates a "pseudo-sideslip angle" through parameters which are easily measured, using sensors equipped in actual vehicles (inertial sensors and steering wheel sensors); this value is introduced in UKF in order to filter noise and to minimize the variance of the estimation mean square error. The estimator has been validated by comparing the observed proposal with the values provided by the CARSIM model, which is a piece of experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle, by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS+UKF-based sideslip angle estimator.

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Sideslip angle estimation based on input–output linearisation with tire–road friction adaptation

Cited by 54 publications

References 14 publications

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

A nonlinear sideslip observer design methodology for automotive vehicles based on a rational tire model

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

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