MPC-Based Integrated Control of Trajectory Tracking and Handling Stability for Intelligent Driving Vehicle Driven by Four Hub Motor

Abstract

Aiming at the problem of trajectory tracking and handling stability of intelligent four hub-motor independent-drive electric vehicle at high speed, an integrated control strategy based on model predictive control (MPC) is proposed. Firstly, a kinematics preview model is established considering the adaptive change of preview distance with longitudinal vehicle speed. Secondly, combined with the proposed preview model and considering the vehicle dynamic characteristics, an upper level controller of MPC is created to track the lateral deviation, heading angle deviation, sideslip angle and yaw rate to obtain the optimal solution of front wheel angle and additional yaw moment, which realize the control of handling stability while ensuring the trajectory tracking accuracy at same time. Then considering the varying road adhesion and the influence of longitudinal slip at the four tires, a lower level controller composed of torque optimal control and sliding mode controller is designed to realize the optimal torque distribution of four hub motors. Finally, through the co-simulation of MATLAB / Simulink and CarSim and the hardware-in-the-loop experiment, it is verified that the proposed integrated control strategy can effectively improve the path tracking accuracy and ensure the vehicle handling stability at high speed. Compared with the general MPC strategy, who does not control the sideslip angle and yaw rate with no preview model, the cumulative lateral tracking error of the integrated control is reduced by 51.9% and 87.7% respectively.