Research on Path Tracking of Intelligent Hybrid Articulated Tractor Based on Corrected Model Predictive Control

To improve the path tracking performance of intelligent hybrid articulated tractors in all working conditions in unmanned operation, a path-tracking control method based on corrected model predictive control is proposed. The kinematic model of the tractor is established by analyzing the tractor’s kinematics. Taking the lateral and longitudinal errors as the target and the speed and articulation angular acceleration as the constraints, a tracking control algorithm based on model predictive control is proposed. In addition, to improve the transient performance of the tractor in the path tracking process, the proportional-integral-derivative controller and fuzzy controller are used to correct the model-predicted output articulation angular acceleration, forming a corrected model predictive control path tracking control method. To verify the effectiveness of the control method, model predictive control is used as a comparison method, and the effectiveness of the proposed method is verified based on the MATLAB 2024a simulation platform. The results show that compared with the MPC algorithm, the speed standard deviation is reduced by 2%, the longitudinal tracking error is reduced by 8%, and the lateral tracking error is reduced by 50%. The proposed method can effectively improve the path-tracking accuracy of the intelligent hybrid tractor.