Abstract：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.

      Key words：trajectory tracking；extended Kalman filter（EKF）；model predictive control（MPC）；CarSim

software

     Format Citation:霍婷婷，贾志龙，晏永,等. 基于扩展卡尔曼滤波器的MPC车辆轨迹跟踪控制[J].电气传动,2026,56(06):76-84. HUO Tingting，JIA Zhilong，YAN Yong,et al. MPC vehicle trajectory tracking control based on extended kalman filter [J].Electric Drive, 2026,56(06):76-84