A Model Predictive Control for Path Tracking of Electronic-Four-Wheel Drive Vehicles

Abstract

This paper proposes a path tracking controller to assist the electronic-four-wheel drive (e-4WD) vehicle in following the desired path profile. It utilizes only readily available in-vehicle sensors and a standalone global positioning system (GPS). Noteworthy, both the lateral distance error and the heading angle error between the desired path profile and the vehicle are calculated using only a standalone GPS. Therefore, the proposed algorithm can be easily applied to mass-produced e-4WD vehicles. A model predictive control (MPC) is selected as the controller type, which derives optimal control input considering both state and input constraints of in-wheel motor (IWM) in the e-4WD vehicle. Due to the advantage of this MPC predicting the vehicle's future dynamic behavior in advance, it is possible to output a more preemptive and stable yaw moment for vehicle path tracking. Finally, a weighted least square (WLS) allocation that is suitable for redundant actuator configuration is utilized to distribute the optimal IWM torques to the front left and right wheels of the e-4WD vehicle. The major contributions of the proposed control algorithm are as follows. 1) It is the first time in this paper that the MPC is applied to path tracking of e-4WD vehicles. 2) The condensed constraint matrix of the proposed MPC has a structure that can be easily applied to the linear programming. Using the CarSim simulation and the real-car based experiment, the effectiveness of the proposed MPC algorithm is verified by a comparative analysis. Thus, the high path tracking accuracy of the proposed algorithm is confirmed.