Trajectory Tracking Control of Intelligent Driving Vehicle Based on Model Predictive Control

Abstract

As the increasing demand for functions, such as autonomous parking and automatic wireless charging, high accuracy of trajectory tracking is required for intelligent driving vehicles. The error accuracy of trajectory tracking obtained by the existing control algorithm is difficult to satisfy the realization of these functions. Therefore, in this paper, the model predictive control (MPC) method is adopted, and the 7-DOF high-fidelity model is used as the plant to improve the error accuracy of trajectory tracking. The steering angle of the front wheel of the vehicle is used as the control input to control the trajectory tracking of the intelligent driving vehicle. 2-DOF dynamic model is used as the prediction model. In order to have direct control over the control increment and to prevent the occurrence of no feasible solution during the execution, the control quantity in the objective function is changed into control increment and relaxation factor is added. Compared with the PID control method, the steering angle and trajectory tracking are simulated in the simulation platform, the proposed control algorithm based on MPC can improve trajectory tracking performance well.