Trajectory Tracking Control of Autonomous Vehicles Combining Model Predictive Control and Dynamic Programming

In this paper, Ackerman steering is used to establish its kinematics modeling based on vehicle motion characteristics, and then the controller is designed according to this prediction model. According to the given trajectory, the reference control quantity of the vehicle is derived. In order to improve the stability of the vehicle, the control quantity and control increment constraints are added. Based on the model predictive control (MPC) idea, the objective function is transformed into a minimization problem of a quadratic function, and the dynamic programming (DP) method is used to solve the optimization problem in a limited rolling horizon. Finally, it is verified in the simulation experiment that the trajectory tracking controller combined with MPC and DP can track the given trajectory well, reduce the tracking error to a certain extent, and significantly improve the driving comfort of the vehicle.