Online Parameter-Reconfigured Model Predictive Control for Integrated Trajectory Tracking of Distributed Four-Wheel Steering Vehicles

To overcome the limitations of conventional model predictive control (MPC) for trajectory tracking of distributed-drive four-wheel-steering (4WS) vehicles, particularly its fixed weighting matrices and prediction and control horizons, this study investigates the integrated trajectory tracking and stability control of an automated distributed-drive electric vehicle equipped with four independently controlled in-wheel motors and a four-wheel-steering system. The main novelty of this study lies in the simultaneous online adaptation of the MPC weighting matrices and reconfiguration of the prediction and control horizons, together with the coordinated integration of four-wheel steering and direct yaw moment control (DYC) within a unified trajectory tracking framework. Unlike conventional adaptive MPC methods that primarily adjust weighting parameters, the proposed adaptive prediction and control horizon adjustment (APCHA) strategy jointly updates the prediction and control horizons according to the integrated tracking error, error variation rate, and control input variation rate. Meanwhile, a fuzzy adaptive weighting mechanism adjusts the MPC weighting matrices online. At the lower control layer, a torque allocation method considering both the tire load ratio and vertical tire loads is employed to realize the required direct yaw moment. Finally, CarSim–Simulink co-simulation is conducted to verify the effectiveness of the proposed control strategy. Simulation results demonstrate that, at a vehicle speed of 60 km/h and a road adhesion coefficient of μ=0.5, the proposed Improved MPC-4WS controller reduces the maximum lateral tracking error by 34.9% compared with the conventional MPC-4WS controller, thereby demonstrating superior trajectory tracking performance. Furthermore, the ablation study verifies the effectiveness of the proposed hierarchical architecture by quantifying the contributions of the DYC module and the optimized torque allocation strategy.