Research on Road Friction Coefficient Estimation Algorithm Based on Extended Kalman Filter

2008 ◽  
Author(s):  
Zhen-jun Sun ◽  
Tian-jun Zhu ◽  
Hong-yan Zheng
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
Coefficient Estimation ◽  
Road Friction ◽  
Road Friction Coefficient

scholarly journals Calculation Algorithm of Tire-Road Friction Coefficient Based on Limited-Memory Adaptive Extended Kalman Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Bin Huang ◽  
Xiang Fu ◽  
Sen Wu ◽  
Song Huang
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Covariance Matrix ◽  
Extended Kalman Filter ◽  
Estimation Accuracy ◽  
Limited Memory ◽  
Road Friction ◽  
Adaptive Extended Kalman Filter ◽  
Road Friction Coefficient ◽  
Real Vehicle

In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

A New Method for Estimating Road Friction Coefficient

2010 ◽  
Vol 139-141 ◽  
pp. 2622-2625
Author(s):  
Fen Lin
Keyword(s):  
Friction Coefficient ◽  
Traffic Safety ◽  
Sliding Mode ◽  
Estimation Algorithm ◽  
Longitudinal Force ◽  
Recursive Least Squares ◽  
Force Estimation ◽  
Road Friction ◽  
Vehicle Activity ◽  
Road Friction Coefficient

Road friction coefficient is a critical component in traffic safety. The estimation of tire–road friction coefficient at tires allows the control algorithm in vehicle activity system to adapt to external driving conditions. This paper develops a new tire–road friction coefficient estimation algorithm based on tire longitudinal force estimation and tire slip estimation. Vehicle tire longitudinal forces are estimated by sliding mode observer combined with Kalman filter. Based on the tire forces estimation, road friction coefficient is estimated by recursive least squares algorithm (RLS). The test conditions which contain different friction level road are established in ADAMS/Car. The conclusions validate the reliability and efficiency of the proposed method for estimating the friction coefficient in different adhesion level roads. The research also indicates the theory of slip slope can also be reappeared in virtual experiment based on ADAMS.

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