Simultaneous estimation of tire side-slip angle and lateral tire force for vehicle lateral stability control

Cheng, Shuo; Li, Liang; Yan, Bingjie; Liu, Congzhi; Wang, Xiangyu; Fang, Jigen

doi:10.1016/j.ymssp.2019.06.022

Cited by 71 publications

(38 citation statements)

References 21 publications

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“…Knowledge of tire side‐slip angle and lateral tire force is crucial to vehicle lateral stability control, which can enhance vehicle handling and passenger safety. In [17], the designed observer can adequately estimate tire side‐slip angles and lateral tire forces of all four wheels. In the meantime, knowledge of the tire–road information was significant for self‐driving cars.…”

Section: Vehicle Dynamics Model and Vehicle–road Relative Position mentioning

confidence: 99%

Lane‐keeping system design considering driver's nervousness via scene analysis

Бин

Zhang

et al. 2020

IET intell. transp. syst

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Section: Vehicle Dynamics Model and Vehicle–road Relative Position mentioning

confidence: 99%

Lane‐keeping system design considering driver's nervousness via scene analysis

Бин

Zhang

et al. 2020

IET intell. transp. syst

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“…To address this issue, observer theory is introduced by some researchers. The observer-based method is a good choice for indirectly obtaining the tire forces through vehicle dynamics, where the construction of a semi-empirical tire model is replaced, such as the sliding-mode observers (SMO) designed in [27], and the PID-based observer and adaptive-sliding-mode observer (ASMO) designed in [28]. However, for the observer-based method, the effect of system noise is not taken into account.…”

Section: Calculation Of Tire Forcesmentioning

confidence: 99%

“…In order to facilitate the construction of lateral force estimator, some simplifications are made on the dual-track vehicle dynamics model mentioned in Section 2.1. This is conducive to simplifying the calculation formulas for state variables [28] with minor effect on the overall idea of vehicle dynamic behavior. Figure 4 shows the established single-track model.…”

Section: Lateral Tire Force Estimatormentioning

confidence: 99%

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Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

Chen

Guo

et al. 2020

Applied Sciences

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Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle's longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.

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“…through equations describing the vehicle dynamics. Many model-based approaches exist, including Luenberger Observers [2], Sliding Mode Observers [3], and the most commonly adopted approach, i.e. the Kalman Filter [4].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

2020

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Vehicle sideslip angle is a key state for lateral vehicle dynamics, but measuring it is expensive and unpractical. Still, knowledge of this state would be really valuable for vehicle safety systems aimed at enhancing vehicle safety, to help to reduce worldwide fatal car accidents. This has motivated the research community to investigate techniques to estimate vehicle sideslip angle, which is still a challenging problem. One of the major issues is the need for accurate tyre model parameters, which are difficult to characterise and subject to change during vehicle operation. This paper proposes a new method for estimating vehicle sideslip angle using an Extended Kalman Filter. The main novelties are: i) the tyre behaviour is described using a Rational tyre model whose parameters are estimated and updated online to account for their variation due to e.g. tyre wear and environmental conditions affecting the tyre behaviour; ii) the proposed technique is compared with two other methods available in the literature by means of experimental tests on a heavy-duty vehicle. Results show that: i) the proposed method effectively estimates vehicle sideslip angle with an error limited to 0.5 deg in standard driving conditions, and less than 1 deg for a high-speed run; ii) the tyre parameters are successfully updated online, contributing to outclassing estimation methods based on tyre models that are either excessively simple or with non-varying parameters. INDEX TERMS Kalman filter; sideslip angle; state estimation; Rational tyre model; vehicle dynamics. List of symbols Symbol Unit Quantity various Dynamic matrix m Vehicle semi-wheelbase m/s 2 Lateral acceleration various Matrix used to work out various Matrix used to work out N/rad Front cornering stiffness N/rad Rear cornering stiffness various Control input vector rad 2 Rational tyre model parameter N/rad Rational tyre model parameter

show abstract

Simultaneous estimation of tire side-slip angle and lateral tire force for vehicle lateral stability control

Cited by 71 publications

References 21 publications

Lane‐keeping system design considering driver's nervousness via scene analysis

Lane‐keeping system design considering driver's nervousness via scene analysis

Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

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