Hierarchical Coordination Control of Distributed Drive Intelligent Vehicle Based on TSMPC and Tire Force Optimization Allocation

An intelligent vehicle hierarchical coordinated control strategy based on time delay state feedback model predictive control (TSMPC) and tire force optimization allocation is presented. Aiming at the problem of insufficient trajectory tracking accuracy and the limited time delay compensation capability of distributed drive intelligent vehicles in complex working conditions, an innovative hierarchical control architecture was designed by establishing vehicle dynamics models and path tracking models. The upper-level controller adopts TSMPC algorithm, which significantly improves the coordinated control ability of path tracking and vehicle stability through incremental prediction model and time–delay state feedback mechanism. The lower-level controller adopts an improved artificial bee colony (IABC) algorithm to optimize tire force allocation, effectively solving the dynamic performance optimization problem of redundant drive systems. Simulation verification shows that compared with traditional model predictive control (MPC) algorithms, TSMPC algorithm exhibits significant advantages in trajectory accurateness, error suppression, and stability control. In addition, the IABC algorithm further improves the trajectory accurateness and stability control performance of vehicles in tire force optimization allocation.