Extended Kalman Filter Based Road Friction Coefficient Estimation and Experimental Verification

2019 ◽  
Author(s):  
Bin Li ◽  
Tao Sun ◽  
Arlene Fang ◽  
Guobiao Song
Keyword(s):  
Kalman Filter ◽  
Friction Coefficient ◽  
Extended Kalman Filter ◽  
Experimental Verification ◽  
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.

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