Research on Distributed Drive Electric Vehicle Lane Change Trajectory Tracking Control Based on MPC

Abstract

<div class="section abstract"><div class="htmlview paragraph">Distributed drive electric vehicles (DDEVs), characterized by independently controllable torque at each wheel, redundant actuators, and highly integrated drive systems, are considered as the optimal platform for achieving intelligent driving with high safety and efficiency. This paper focuses on the trajectory tracking and lateral stability coordination control problems in high-speed emergency collision avoidance and autonomous lane change scenarios for DDEVs. A trajectory tracking control algorithm is proposed based on model predictive control (MPC) and coordinated optimization of distributed drive torques. The method adopts a hierarchical control architecture. Firstly, the upper-level trajectory planning layer calculates the lane change trajectory data. Based on the trajectory planning results, the middle-level controller designs a time-varying linear model predictive control method to solve the desired front wheel steering angle and additional yaw moment. Then, considering the modeling uncertainty of the steering actuator and road disturbances, a front wheel steering angle tracking strategy based on sliding mode variable structure control is designed. The lower-level controller aims to minimize tire loading rate to maximize vehicle stability margin and utilizes an active set algorithm to achieve four-wheel torque optimization allocation. Finally, a four-wheel hub motor-driven electric vehicle is taken as the research object. The effectiveness and real-time performance of the proposed control method are validated through simulations on the CarSim/Simulink co-simulation platform under high-speed lane change and double-lane change conditions.</div></div>