Abstract
KEYWORDS –Yaw rate prediction using EKF, Driver's intention identification, Reference model modification, Vehicle stability control, Four-wheel-drive-electric-vehicle
ABSTRACT –In critical situation, for the vehicle stability control problem, the conventional reference model(RM) only using steering wheel angle to reflect driver's intention has some drawbacks in emergent steering(ES) and emergent alignment(EA). In this work, the RM modification strategy based on yaw rate prediction and driver's intention recognition under emergent obstacle avoidance situation was proposed. First, in ES stage, a extended Kalman filter(EKF) was used to predict the yaw rate for modifying the yaw rate tracking error. In EA stage, combining steering wheel angle and its speed, the driver's EA intention was recognized. The RM modification strategy based on steering operation index(SOI) was presented. Second a LQR model following controller with tire cornering stiffness adaption was used to generate direct yaw moment for tracking desired modified yaw rate and desired sideslip angle. Finally based on the four-wheel-drive-electric-vehicle(FWDEV), double lane change tests and slalom tests were conducted to compare the results using modified RM with the results using normal RM. The experimental tests have proved the effectiveness of the RM modification strategy based on yaw rate prediction and driver's intention recognition.