Integrated PSO-MPC Control for Trajectory Tracking and Stability in Distributed Drive Electric Commercial Vehicles

Distributed drive electric commercial vehicle exhibits strong coupling and nonlinear characteristics in longitudinal and lateral dynamics under complex driving conditions, which pose significant challenges for trajectory tracking control. To achieve a coordinated improvement in trajectory tracking accuracy and vehicle stability, this paper proposes a multiobjective integrated control strategy based on particle swarm optimization (PSO) and model predictive control (MPC). First, a prediction model is developed that incorporates coupled longitudinal-lateral dynamics and nonlinear tire characteristics. Within the MPC framework, the accuracy of trajectory tracking, vehicle stability, and actuator constraints are jointly considered to compute optimal control inputs. Subsequently, PSO is employed to adaptively optimize key MPC parameters - such as the prediction horizon, control horizon, and objective function weights - offline, enabling the controller to adapt to varying operating conditions while balancing control performance and computational efficiency. Simulation results demonstrate that the proposed strategy enhances trajectory tracking accuracy and longitudinal-lateral stability.