Simulation Design of Intelligent Vehicle Transverse Control System

The vehicle trajectory tracking control function plays a decisive role for the vehicle to achieve driverlessness. In this paper, a 6-state variable vehicle high-speed control model based on vehicle dynamics is established. Then a nonlinear simplified model of the vehicle is built by linearizing assumptions on tires and small angle approximation assumptions. Through the analysis of the vehicle model and constraints, this paper chooses to transform the nonlinear system of the vehicle into a linear time-varying system. Then the solved control increments are applied to the vehicle through the model predictive control (MPC) algorithm, which can realize the closed-loop control of MPC. In the simulation process, this paper chooses double-shifted lines as the driving conditions for the analysis of the trajectory tracking control performance of unmanned vehicles. In order to verify the superiority of MPC controller in tracking control, the pre-scanning optimal control (PSOC) algorithm in CarSim is used as a comparison in this paper. The simulation results show that the model predictive controller designed in this paper has similar tracking accuracy with the pre-scanning control at low speed, but at high speed, the MPC controller has significant tracking control advantages, with strong adaptability and robustness to the tracking accuracy, body stability control and external disturbance.