A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control

Lian, Yufeng; Feng, Wenhuan; Liu, Shuaishi; Nie, Zhigen

doi:10.3389/fnbot.2023.1229808

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…However, such a method based on the response recognition of tires is affected by many external uncertainties due to the complexity of the generation mechanism of the tire noise, and sometimes it is insurmountable to accurately identify the adhesion coefficients. Recently, some identification methods on the basis of visual information have been proposed in the current study [ 25 , 26 , 27 , 28 ]. For example, given the nondeterminacy of kinematic models and deep-learning models, an image-based fusion estimation method by virtue of the virtual sensing theory was put forward to exactly realize the identification of the road surface condition in reference [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

Zhang,

Song,

et al. 2024

Sensors

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Because of its uneven and large slope, unstructured pavement presents a great challenge to obtaining the adhesion coefficient of pavement. An estimation method of the peak adhesion coefficient of unstructured pavement on the basis of the extended Kalman filter is proposed in this paper. The identification accuracy of road adhesion coefficients under unstructured pavement is improved by introducing the equivalent suspension model to optimize the calculation of vertical wheel load and modifying vehicle acceleration combined with vehicle posture data. Finally, the multi-condition simulation experiments with Carsim are conducted, the estimation accuracy of the adhesion coefficient is at least improved by 3.6%, and then the precision and effectiveness of the designed algorithm in the article are verified.

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Section: Introductionmentioning

confidence: 99%

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

Zhang,

Song,

et al. 2024

Sensors

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Trajectory tracking multi-constraint model predictive control of unmanned vehicles based on sideslip stiffness estimation with XGBoost algorithm

Wang,

Zheng,

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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When the vehicle is driving at high speed on a slippery road, the sideslip stiffness of the tire which is closely related to the lateral force of the tire, will vary with the change of road conditions, tire inflation pressure, vertical load, and other factors. If the tire sideslip stiffness is set to a fixed value in the control system based on the model design, the uncertainty of the sideslip stiffness will cause a large error with the actual application. Therefore, in order to improve the trajectory tracking accuracy of the vehicle on the slippery road surface under the premise of ensuring the stability of the vehicle, an intelligent vehicle trajectory tracking control strategy considering the influence of sideslip stiffness is designed in this paper. In order to solve the multiple constraints of the vehicle driving on the low-adhesion road surface, a trajectory tracking controller based on the multi-constraint model predictive control algorithm is designed, and then the tire sideslip stiffness estimation strategy is designed based on the XGBoost algorithm, and the estimated sideslip deviation stiffness is added to the trajectory tracking control model. Carsim and MATLAB/Simulink are used to perform co-simulation to verify the accuracy of the proposed tire sideslip stiffness estimation and the tracking performance of the trajectory tracking controller. In order to further verify the reliability of the research content, the relevant working condition tests are carried out on the hardware-in-the-loop platform based on Carsim and LabVIEW, and the effectiveness of the trajectory tracking controller considering the influence of sideslip stiffness is verified.

show abstract

A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control

Cited by 2 publications

References 26 publications

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

Trajectory tracking multi-constraint model predictive control of unmanned vehicles based on sideslip stiffness estimation with XGBoost algorithm

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