A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

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  • Hongxiao Yu
    Beijing Key Laboratory of Traffic Engineering, College of Transportation, Beijing University of Technology, Beijing, China
  • Jianmin Duan
    Beijing Key Laboratory of Traffic Engineering, College of Transportation, Beijing University of Technology, Beijing, China
  • Saied Taheri
    Department of Mechanical Engineering, Center for Tire Research (CenTiRe), Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
  • Huan Cheng
    Beijing Key Laboratory of Traffic Engineering, College of Transportation, Beijing University of Technology, Beijing, China
  • Zhiquan Qi
    School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

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<jats:p> Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios. </jats:p>

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