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