摘要
以回转支承试验台为研究对象,基于高增益扰动观测器(HGDOB)与径向基函数神经网络(RBFNN)提出了回转支承试验台液压加载控制方法。对控制对象——单出杆油缸与伺服阀进行数学建模,应用径向基函数神经网络对模型中油缸的非线性摩擦进行逼近,应用高增益扰动观测器对外部扰动和噪声进行观测,进而提高系统实际输出的加载力逼近期望输出的性能。通过Matlab/Simulink软件对所提出的回转支承试验台液压加载控制方法进行仿真分析,确认这一方法提高了控制器的动态跟踪与抗干扰能力,具有实用价值。
Taking the slewing bearing test rig as the research object,a hydraulic loading control method for the slewing bearing test rig was proposed based on HGDOB and RBFNN.Mathematical modeling of the control object single rod cylinder and servo valve was carried out,RBFNN was used to approximate the nonlinear friction of the cylinder in the model,HGDOB was used to observe external disturbances and noise,and then the performance that the actual output load force of the system close to the expected output was improved.Through Matlab/Simulink software,the proposed hydraulic loading control method for the slewing bearing test rig was simulated and analyzed.It is confirmed that this method improves the dynamic tracking and anti interference ability of the controller with practical value.
作者
都璐远
陈捷
杨贵超
Du Luyuan;Chen Jie;Yang Guichao
出处
《机械制造》
2021年第6期75-79,共5页
Machinery
基金
江苏省自然科学基金资助项目(编号:BK20130941)。
