To enhance the efficiency of tractor operation, the longitudinal and lateral dynamic control of a self-driving tractor is studied in this paper, and a control system that decouples control of the longitudinal and lateral movement of the tractor is proposed. The trajectory controller calculates the desired speed and desired yaw rate signals of each subsystem, and a PID controller regulates the longitudinal speed of the tractor. A pure pursuit algorithm calculates the desired front wheel angle of the tractor. To decrease the system time delay in the automatic steering system, an automatic steering scheme based on improved Smith predictive control is proposed. Through decoupling control of the longitudinal and lateral controllers, the tractor’s path tracking performance is assured.

For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision‐making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective.

Optimization design and experiment on feeding and chopping device of silage maize harvester

For the problem of lacking reliable values of soil parameters required for dynamic simulation analysis of soil under high-speed tillage, no-tillage soil was taken as the research object and its parameters were calibrated using the discrete element method in this study. The Edinburgh Elasto-Plastic Adhesion (EEPA) model was determined as the soil contact model by obtaining the loading force-deformation relationship through uniaxial sealing compression tests. The regression equation was established using Plackett-Burman test and quadratic orthogonal rotation test, and the interaction effects were analyzed. The measured values of the pile angle and strain were obtained through the pile angle test and the uniaxial seal compression test, and the optimal solution was carried out. Further, the simulated values and the measured values under the optimal parameters were compared and verified, the result showed that the error values were all less than 1%. Finally, the soil model was used for high-speed tillage simulation analysis, and the obtained soil particle displacement and groove width were compared with the measured values. It was found that the ditch width error value was 3.04% and the soil displacement was basically the same. This study proved that the contact model parameters were relatively reliable, which could provide theoretical reference for the dynamic characteristics of soil during high-speed cultivation of no-tillage soil.