Maximum Tire Road Friction Estimation Based on Modified Dugoff Tire Model

Chen, Long; Bian, Mingyuan; Luo, Yugong; Li, Keqiang

doi:10.1109/maee.2013.24

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…𝐹 cf and 𝐹 𝑐𝑟 are the lateral forces on the front and rear wheels, 𝐹 𝑙𝑓 and 𝐹 𝑙𝑟 are the longitudinal forces on the front and rear wheels. For the sake of simplicity in calculation, this paper adopts the linear Dugoff tire model [24].…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

Research on intelligent vehicle path tracking control based on preview distance optimization

Huang

Zhang

et al. 2023

Preprint

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This paper proposes an intelligent vehicle lateral-vertical cooperative control method based on optimized preview distance to address the coupling and conflict issues between the path tracking system and the handling stability in the process of controlling the motion of intelligent vehicles. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicles. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the "human-vehicle-road" system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, HIL platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 meters, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions. The research findings provide a theoretical reference for the lateral and vertical coordinated control of intelligent vehicles in the future.

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Section: Vehicle Dynamics Modelmentioning

confidence: 99%

Research on intelligent vehicle path tracking control based on preview distance optimization

Huang

Zhang

et al. 2023

Preprint

Self Cite

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“…This tire model formula used to model the tire forces as in eq.10 and eq.12. To calculate longitudinal force F xi and lateral force F yi ,This formula requires some variables: vertical force (F zi ), longitudinal stiffness (C σ ), cornering stiffness (C α ), slip ratio (σ), tire side slip (α), and friction coefficient (µ) [14,15]. Those vertical forces are calculated from eq.7 and eq.9 that are derived from moment equation in each axis shown in Figure 5.…”

Section: Estimating Cornering Stiffness From Nonlinear Tire Modelmentioning

confidence: 99%

Dynamic Analysis of Narrow Tilting Three Wheeled Vehicle (NTTWV) with LQG control

Adhim

Wasiwitono

2017

Int. Journal of Mech. Eng. Sc.

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The need for compact vehicles will increase in urban areas in the future. This paper analyses the dynamics of the actively controlled narrow tilting three-wheel vehicle (NTTWV). The vehicle has 3 wheels, 2 front wheels as steering wheels and the rear wheel as traction wheel. Active tilting system is used to give desired roll angel ϕ that can resist the centrifugal force to maintain the vehicle stability. The simulation result showed that the NTTWV rolls only 76% of ideal motorcycle rolling angle at same velocity and turning radius. The improvement of critical speed compared to non-tilting three-wheeled vehicle at δ = 5 • and δ = 10 • are 193% and 171%.

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“…On the other hand, the frictional force-wheel slip curve of the Dugoff et al model shows a continuously increasing trend with increasing wheel slip ratio or side-slip ratio and has no peak point, which forms the conclusion that this model does not reflect the actual principles of the tyre-road frictional forces. 18 Therefore, to combine the benefits of both tyre models, in this paper the modified Dugoff et al model presented by Bian et al 14 is adopted.…”

Section: Vehicle Modelmentioning

confidence: 99%

Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion

Chen

Bian

Luo

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle active safety systems can benefit significantly from a priori knowledge of the road conditions. This paper proposes a real-time tyre-road friction estimation algorithm based on an unscented Kalman filter and mean-square-errorweighted fusion, using measurements related to the electric vehicle and in-wheel motors. Because the modules are mutually independent, the dimensionality of the matrix used in the filtering process can be reduced to shorten the computational period. The approach can also work more effectively in various conditions than those techniques which focus on a specific direction of the vehicle dynamics. First, a modified Dugoff et al. tyre model is considered to express the non-linear characteristics of the tyres. Then, to observe the longitudinal tyre forces and the lateral tyre forces, a parameter identification technique that utilizes the information from an inertial sensor and a gyroscope is introduced. An unscented Kalman filter rather than the traditional linear filter is adopted to obtain higher accuracy in the saturation region. After the relationship between the mean square error and the side-slip angle or the slip ratio is taken into account, the final identified value is set by combining the results from the longitudinal unscented Kalman filter and the lateral unscented Kalman filter in order to improve the accuracy and the robustness further. Experiments conducted on both a dry road and a slippery road verify that the method based on an unscented Kalman filter can identify different tyre-road friction levels.

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Maximum Tire Road Friction Estimation Based on Modified Dugoff Tire Model

Cited by 15 publications

References 7 publications

Research on intelligent vehicle path tracking control based on preview distance optimization

Research on intelligent vehicle path tracking control based on preview distance optimization

Dynamic Analysis of Narrow Tilting Three Wheeled Vehicle (NTTWV) with LQG control

Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion

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