Nonlinear State and Tire Force Estimation for Advanced Vehicle Control

Abstract

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. The filtered histories of forces and motion can be used to construct tire force models through off-line analysis, and both tire force estimates and state estimates are available for real time control. No prior knowledge of tire force characteristics or external factors that affect vehicle motion is required for the nonlinear estimation procedure. Simulation of a simple slip control braking system using slip and slip angle estimates for feedback demonstrates the effectiveness of the extended Kalman filter in providing adequate state estimates for advanced control of ground vehicles. © 1995 IEEE