基于扩展卡尔曼滤波器的MPC车辆轨迹跟踪控制OA

To enhance the trajectory tracking control stability of driverless vehicles under real-world noise interference,a vehicle trajectory tracking controller based on model predictive control(MPC)integrated with the extended Kalman filter algorithm(EKF-MPC)was constructed.Firstly,a vehicle dynamics model was established,and the trajectory tracking controller,objective function,relevant constraint conditions were designed to address nonlinear vehicle characteristics and measurement noise.The MPC algorithm was applied to solve for the optimal control inputs at each sampling instant.Meanwhile,the extended Kalman state estimator dynamically updated its gain matrices to calculate the posterior state matrix,effectively counteracting the adverse effects of vehicle nonlinearity and state measurement noise.Joint simulation verification was conducted using Matlab and CarSim,and the results show that compared with the conventional MPC controller,the EKF-MPC controller improves safety by 2.3%and feasibility by 37.7%across different road sections.Its stability is significantly enhanced,these findings provide strong support for the development of vehicle trajectory tracking control technology.