No abstract

In terms of the research on electric power steering system, there are some problems, such as model uncertain and external interference. However, it is difficult for a general controller to deal with those problems as well as the performance of the system. Therefore, the paper is to propose a control method based on generalized internal model control, which is based on feedback and Youla parameterization, including performance controller and compensation controller. The performance controller is used to make the electric power steering system work well, while the compensation controller is used to solve model uncertain and external interference. First of all, the paper is to establish the model of electric power steering system, introduce the 2DOF vehicle and the tire model, set up the state space of the electric steering system including model uncertain and interference and design the generalized internal model controller. Finally, the simulation and the hardware-in-the-loop experiment are carried out to verify the controller. The results show that the proposed controller takes advantages of better control performance, solving model uncertain and external interference, and improving the performance and robustness of the system.

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.

During silage harvesting, silage corn stalk is compressed by a feeding device and fed into the shearing device to be sheared into qualified segments to make silage fermentation easier. To optimize the working quality of the existing silage harvester and reduce energy consumption, it’s necessary to make a comprehensive analysis of the longitudinal compressing and shearing properties of the silage corn stalks and get a reliable shearing model. According to the different structural properties of the silage corn stalks, the main factors affecting the shearing energy consumption and their levels were obtained by compressing and shearing tests on internodes and nodes in this paper. The results of three-level and three-factor experiments showed that the overall shearing energy consumption for nodes was much higher than that for internodes. Compressing the silage corn stalk to some extent before shearing at the loading direction of 0° and lower shearing speed was beneficial to saving energy during the process of shearing off the silage corn stalk. The reduced energy requirements of the silage corn stalk could be exploited advantageously to present new reference for the feeding and cutting mechanisms of silage harvester. The research results can provide a reference for silage corn harvesting.