Research on hierarchical collaborative control of driverless hybrid tractor trajectory tracking and energy management system

Abstract

The energy consumption economy and trajectory tracking effect of driverless hybrid tractors are important performance indicators. Currently, separate methods are used to control trajectory tracking and energy management independently while ignoring the interaction between the two variables, resulting in reduced tractor performance. To address this problem, this study proposes a hierarchical collaborative control method (HCCM) for tractor trajectory tracking and energy management. Firstly, construct the longitudinal and transverse kinematics model. Taking the tractor's front wheel rotation angle and speed change as constraints and minimizing lateral deviation as the goal, a trajectory tracking strategy (upper layer) is proposed based on Model Predictive Control. Secondly, with the motor and diesel engine speed and torque control variables, power battery SOC as a constraint, and minimizing the total energy consumption cost as the goal, a three-stage dynamic programming energy management strategy (lower layer) is designed based on dynamic programming. On this basis, using velocity as an interactive variable and then integrating upper-layer and lower-layer control strategies, a dual-system HCCM is proposed to simultaneously improve the tractor's trajectory tracking accuracy and energy consumption economy. Finally, mutually independent pure pursuit tracking algorithms and power following energy management strategies are selected for comparison. Simulation shows that the proposed dual-system hierarchical collaborative control method improves trajectory tracking performance and reduces the total energy cost of the entire machine. Under rotary tillage conditions and transportation conditions, the trajectory tracking accuracy increases by 75.02% and 80.36%, and the cost of total energy consumption reduces by 8.48% and 9.01%, respectively.