Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient

TLDR: The parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time.

Abstract: The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.