A Estimation Method of Road Adhesion Coefficient Based on A New Adaptive Square Root Cubature Kalman Filter

The intelligent chassis control system plays a crucial part in improving the safety, the stability and the driving comfort. The timely and precise estimation of the road adhesion coefficient is the premise of realizing the control system. Since this parameter cannot be accurately measured by on-board sensors, and the current indirect measurement methods are inevitably affected by noise in complex driving conditions, how to achieve timely and precise estimation of road adhesion coefficient is the focus of current research. In this paper, a new adaptive square root cubature Kalman filter (ASRCKF) is designed to estimate the pavement adhesion coefficient. Firstly, the four-wheel vehicle dynamics model and tire model are established. Then, the cubature Kalman filter is improved, the covariance matrix is processed by using the square root form. At the same time, the residual is the difference between the real time measured value and the measured predicted value. A process noise covariance matrix update rule is used to construct ASRCKF. Finally, used the Carsim and MATLAB-simulink joint simulation platform, the experimental results indicate that the ASRCKF can better adapt to the driving environment with sudden change of road adhesion coefficient, verifying the effectiveness and superiority of this algorithm.