Nonlinear Tire Force Estimation and Road Friction Identification: Simulation and Experiments,

Thispaper applies extended Kalman–Bucyfiltering (EKBF)and Bayesianhypothesisselectionto estimate motion, tire forces, and road coefficientof friction (p) of vehicle8on asphalt surfaces.The EKBFestimatesthe state and tire forcesof an eight-degree-of-freedomvehiclefrom vehicle-mountedsensors.The filter requiresno a prioriknowledgeof p and does not require a tire force model. Resultingforce, slip, and slip angle egtimatesare compared statistically with those that result from a nominal analytic tire model to select the most likelyp from a set of hypothesizedvalues.The method8have application to both off-lineconstruction of tire models and development of vehiclecontrol systemgthat require p. Both simulation results and results of applying estimation methods to field test data are presented. Simulation results show excellent convergence and accuracy of ~ estimates, and results of processingfield test data demonstrate the ability to construct useful tire models. Computation and sensor requirements, and robustness of the v identification algorithm are considered. Q 1997 Ekevier ScienceLtd.