Research on Hierarchical Motion Control of Corner Module Configuration Intelligent Electric Vehicle

Abstract The intelligent vehicle corner module system, which integrates four-wheel independent drive, independent steering, independent braking and active suspension, can accurately and efficiently perform vehicle driving tasks and is the best carrier of intelligent vehicles. Nevertheless, too many angle/torque control inputs make control difficult and non-real-time. In this paper, a hierarchical real-time motion control framework for corner module configuration intelligent electric vehicles is proposed. In the trajectory planning module, an improved driving risk field is designed to describe the surrounding environment's driving risk. Combined with the kinematic vehicle-road model, model predictive control (MPC) method, spline curve method, the local reference trajectory of safety, comfort and smoothness is planned in real time. The optimal steering angle is determined using MPC method in path tracking module. In the motion control module, a feedforward-feedback controller assigns the optimal steering angle to the front/rear axles, and an angle allocation controller distributes the target angles of the front/rear axles to four steered wheels. Finally, the PreScan-Simulink-CarSim joint simulation environment is established for conducting the human-in-the-loop emergency obstacle avoidance experiment. It took only 0.005 s for the hierarchical motion control system to determine its average solution time. This proves the effectiveness of the hierarchical motion control system.