Estimation of road friction coefficient using extended Kalman filter, recursive least square, and neural network

This paper presents an appropriate method for estimating road friction coefficient. The method uses measured values from wheel angular velocity and yaw rate sensors of a vehicle so that it could estimate the road friction coefficient. The estimation process is done in three steps: first, vehicle lateral and longitudinal velocities along with yaw rate value are identified by an extended Kalman filter observer when lateral acceleration and yaw rate values are subjected to process and measurement noises, respectively. Then, lateral and longitudinal tire forces are estimated using a recursive least square algorithm so that to be used in a neural network designed based on well-known Magic Formula tire model. In the final stage, using a multilayer perceptron neural network and estimated values of the previous stages, the road friction coefficient is estimated. Finally, the set of estimators is evaluated using 14 degrees of freedom full vehicle dynamic model and the obtained results are compared with their actual values of vehicle model for two different maneuvers of vehicle.