摘要
针对液静压轴承外部负载改变时、油膜厚度发生变化、从而出现轴承的承载力及阻尼性降低的问题,设计等油膜厚度的主动式控制方法。以比例压力阀作为压力补偿元件,控制回路以供油压力、工作台位移、油腔流量及工作压力作为回馈。计算保持稳态补偿膜厚差所需的供压压力差值作为压力阀的PID控制参数值,再结合类神经网络经训练参数找出最佳PID控制器参数,进行位移补偿控制,达到稳态等膜厚控制。实验结果表明,神经网络PID控制器比其他神经网络控制器具有更快的暂态振荡收敛,并迅速达到等膜厚的目标。
Aiming at the problem that the oil film thickness changes when the hydrostatic bearing external load changes, and the bearing capacity and damping are reduced, an active control method for the oil film thickness was designed. The proportional pressure valve was used as a pressure compensation element, and the control circuit was fed back with oil supply pressure, table displacement, oil chamber flow and working pressure. The supply pressure difference required to maintain the steady-state compensation for the film thickness difference was calculated as the PID control parameter value of the pressure valve, then combining the neural network-trained parameters, the best PID controller parameters were found, displacement compensation control was performed to achieve steady state equal film thickness control. The experimental results show that the neural network PID controller has faster transient oscillation convergence than other neural network controllers, and quickly reaches the target of equal film thickness.
作者
荀珂
冉翠翠
XUN Ke;RAN Cuicui(Department of Information Engineering,Henan Vocational College of Agriculture,Zhengzhou Henan 451450,China;Nanyang Normal University,Nanyang Henan 473061,China)
出处
《机床与液压》
北大核心
2021年第10期56-59,64,共5页
Machine Tool & Hydraulics
关键词
液静压轴承
等膜厚控制
PID控制
神经网络算法
Hydrostatic bearing
Equal film thickness control
PID control
Neural network algorithm
作者简介
荀珂(1982-),女,学士,讲师,主要研究方向为计算机控制。E-mail:xk_hnny@sina.com。
