Model predictive control for multi-axis steering trajectory tracking strategy of heavy-duty articulated vehicle with vehicle status accurate estimation

Multi-axle articulated vehicle has great engineering transportation value However, due to the large size of the vehicle, research on its intelligent driving still mainly relies on kinematic models, which limits the further increase of vehicle speed and the accuracy of trajectory control. This study established a three-degree-of-freedom dynamic model and kinematic model respectively for the tractor and trailer of articulated vehicles. And a model predictive control(MPC) for multi-axis steering trajectory tracking strategy of heavy-duty articulated vehicle with vehicle status accurate estimation achieved by stratified unscented Kalman filter(SUKF) was designed. Based on Simulink-Trucksim co-simulation, the results confirmed that under the speed range of 10km/h to 60km/h, the SUKF mode can accurately describe the side slip angle, yaw rate and position information of tractor and trailer respectively. With speed increasing, errors enhanced but them can meet the requirements at high speed. Based on vehicle status estimated by SUKF, MPC controller can effective achieve articulated vehicle multi-axis active steering and trajectory tracking. The average maximum lateral error was only around 0.5 m for tractor when speed in the range of 10km/h and 50km/h. On low adhesion road surface, CG side slip angle and lateral acceleration were shortened by 5–10° on average obviously, and the hinge angle was also reduced around 2° under MPC controller.