Four-wheel steering (4WS) vehicles have better stability control and path following performance than front-wheel steering (FWS) vehicles. Aiming at this characteristic, a new four-wheel active steering control strategy is proposed. For intelligent vehicle path tracking and nonlinear vehicle system state estimation, a path tracking control algorithm based on the MPC algorithm is designed to analyze the stability of the vehicle and set up constraints to achieve accurate tracking of the reference path. Automotive dynamic control systems require information on system variables. For example, the sideslip angle of electric vehicles cannot be measured directly. Based on UKF theory and the information input of low-cost sensors on the vehicle, an estimator is designed to estimate vehicle sideslip angle and yaw rate. According to the difference between the estimated value and the ideal value of the vehicle state, the LQR optimal controller is designed to realize the optimal control of the front and rear steering. And compared with the dynamic simulation results of front-wheel steering, proportional control four-wheel steering and yaw rate feedback four-wheel steering. The experimental results of the simulation platform show that the path tracking 4WS state feedback optimal control method has good lateral control stability and path tracking accuracy.

四轮转向 (4WS) 车辆比前轮转向 (FWS) 车辆具有更好的稳定性控制和路径跟随性能。针对这一特点，提出了一种新的四轮主动转向控制策略。针对智能车辆路径跟踪和非线性车辆系统状态估计，设计了一种基于MPC算法的路径跟踪控制算法，分析车辆的稳定性并设置约束条件，实现对参考路径的精确跟踪。汽车动态控制系统需要有关系统变量的信息。例如，电动汽车的侧滑角不能直接测量。基于UKF理论和车辆上低成本传感器的信息输入，设计了一个估计器来估计车辆的侧滑角和横摆角率。根据车辆状态估计值与理想值的差异，设计LQR最优控制器，实现前后转向的最优控制。并与前轮转向、比例控制四轮转向和横摆率反馈四轮转向的动力学仿真结果进行对比。仿真平台的实验结果表明，路径跟踪4WS状态反馈最优控制方法具有良好的横向控制稳定性和路径跟踪精度。