自适应预测时域参数MPC车辆轨迹跟踪控制OACSTPCD

To improve the accuracy and stability of trajectory tracking control of unmanned vehicles at different speeds,the traditional fixed prediction horizon Model Predictive Control(MPC)controller is optimized and a vehicle trajectory tracking control strategy based on adaptive prediction horizon parameter MPC is proposed in this paper.The grey relational method is employed to determine the optimal horizon parameters of MPC under different target speed conditions.The Fourier approximation method is employed to fit the prediction horizon parameters,and the semi-empirical model predicting the horizon parameters with the change of vehicle speed is obtained by combining the vehicle dynamics model and MPC algorithm.The model selects the relative optimal prediction horizon according to the change of the target speed of the vehicle trajectory tracking.Our simulation comparison test and real vehicle test show the adaptive prediction horizon parameter MPC controller reduces the trajectory tracking error and improves the solution speed.The mean yaw angle deviation is reduced by 14.7%and the mean lateral deviation is down by 21.7%.Meanwhile,it is highly adaptable to different vehicle speeds.