Soft sensor application in vehicle yaw rate measurement based on Kalman filter and vehicle dynamics

Abstract

The accurate measurement of vehicle yaw rate is vital for vehicle dynamics control, such as yaw control and traction control. Generally, vehicle yaw rate is measured by gyro that costs too much to be used commercially as an on-vehicle sensor. Based on soft sensor technique in inferential control theory, a novel method for the estimation of vehicle yaw rate is proposed. The estimation is based on Kalman filter and 2 degree-of-freedom vehicle dynamic models to realize the estimation of way rate of linear minimize mean square error. Results of simulation and experiment show an accurate and low-cost estimation of yaw rate is achieved and soft sensor estimation method is feasible in measurement of vehicle state.