Model predictive control for vehicle stability via active rear steering and breaking

Vehicle active safety is to help guarantee the safety of cars and receives increasing attentions in recent years. In this paper, we proposed a new approach to improve yaw stability and agility of a vehicle in high speed situations. In order to consider the saturation characteristics of the tire, a stiffness weighted tire model is proposed. Based on the tire model, a linearized nonlinear vehicle model is built, and this model is easy to be used in the design of control logic or state estimator. Based on this vehicle model, the model predictive control (MPC) algorithm by using the differential breaking and active rear steering is presented. There are two sub-controllers working together, the top controller calculates the additional yaw moment and rear steering angle; the lower sub-controller distributes the tire forces by using the quadratic programming method. The control performances of MPC method were verified in the Matlab simulation environment. The simulation results clearly show that the proposed control algorithm can enhance the vehicle stability and safety effectively especially on difficult road situation.