Research on Algorithm of Vehicle Trajectory Model Predictive Controller

An improved model predictive controller（MPC） was proposed to improve the tracking accuracy and vehicle stability of driverless vehicles. Firstly, in order to solve the vehicle instability problem caused by the strong nonlinear characteristics of tire side stiffness under high-speed turning conditions, a tire dynamic side stiffness estimator was established for real-time estimation. On this basis, the improved particle swarm optimization algorithm was used to optimize the time domain parameters of the existing model prediction controller to improve the tracking accuracy and stability of the algorithm. Finally, in order to verify the effect of the controller, the appropriate working conditions were selected for co-simulation test. The results show that the tracking accuracy error and yaw Angle error of the improved controller are reduced to 4. 9% and 2. 6% respectively under the condition of high-speed turning, which are 67. 8% and 62. 3% higher than those of the traditional model forecast controller, and 55. 8% and 58%higher than those of the controller optimized by fuzzy control algorithm.