Research on Path Tracking of Articulated Steering Tractor Based on Modified Model Predictive Control

Abstract

With the development of agricultural mechanization and information technology, automatic navigation tractors are becoming a more common piece of farm equipment. The accuracy of automatic navigation tractor path tracking has become critical for maximizing efficiency and crop yield. Aiming at improving path tracking control accuracy and the real-time performance of the traditional model predictive control (MPC) algorithm, the study proposed an adaptive time-domain parameter with MPC in the path tracking control of the articulated steering tractor. Firstly, the kinematics model of the articulated steering tractor was established, as well as the multi-body dynamics model by RecurDyn. Secondly, the genetic algorithm was combined with MPC. The genetic algorithm was used to calculate the optimal time domain parameters under real-time vehicle speed, vehicle posture and road conditions, and the adaptive MPC was realized. Then, path tracking simulations were conducted by combining RecurDyn and Simulink under different path types. Compared with the traditional MPC algorithm under the three paths of U-shaped, figure-eight-shaped and complex curves, the maximum lateral deviations of the modified MPC algorithm were reduced by 59.0%, 24.9% and 13.2%, respectively. At the same time, the average lateral deviation was reduced by 72%, 43.5% and 20.3%, respectively. Finally, the real path tracking tests of the articulated steering tractor were performed. The test results indicated that under the three path tracking conditions of straight line, front wheel steering and articulated steering, the maximum lateral deviation of the modified MPC algorithm was reduced by 67.8%, 44.7% and 45.1% compared with the traditional MPC. The simulation analysis and real tractor tests verified the proposed MPC algorithm, considering the adaptive time-domain parameter has a smaller deviation and can quickly eliminate the deviation and maintain tracking stability.