Nonlinear tire force estimation and road friction identification: simulation and experiments

摘要: Abstract This paper applies extended Kalman-Bucy filtering (EKBF) and Bayesian hypothesis selection to estimate motion, tire forces, road coefficient of friction (μ) vehicles on asphalt surfaces. The EKBF estimates the state forces an eight-degree-of-freedom vehicle from vehicle-mounted sensors. filter requires no a priori knowledge μ does not require force model. Resulting force, slip, slip angle are compared statistically with those that result nominal analytic model select most likely set hypothesized values. methods have application both off-line construction models development control systems μ. Both simulation results applying estimation field test data presented. Simulation show excellent convergence accuracy estimates, processing demonstrate ability construct useful models. Computation sensor requirements, robustness identification algorithm considered. © 1997 Elsevier Science Ltd.