Nonlinear Estimation of Vehicle State and Tire Forces

Vehicle motion and tire force histories are estimated from an incomplete, noise-corrupted measurement set using an extended Kalman filter. A nine degree-of-freedom vehicle model and an analytic tire force model are used to simulate true vehicle motion, and a five degree-of-freedom vehicle model is used in the estimator. Simulation demonstrates that real-time filtering can provide state estimates required for advanced feedback control of ground vehicles. In addition, filtered histories of forces and motion can be used to construct tire models through off-line analysis. No prior knowledge of tire force characteristics or external factors that affect vehicle motion is required.