摘要
液压起重机在驱动小负载时,导致液压泵输出功率较大,造成能源浪费。对此,提出了改进径向基函数(RBF)神经网络PID控制方法,并对液压泵输出功率进行仿真。创建液压起重机平面简图,设计了负载敏感平衡阀,推导液压起重机驱动动力学方程式。对传统RBF神经网络结构进行改进,设计了动态自适应RBF神经网络PID控制器,采用Matlab软件对液压起重机改进RBF神经网络控制效果进行仿真。结果表明:在空载或轻载工况下,悬臂在上升过程中,采用RBF神经网络PID控制与改进RBF神经网络PID控制方法,液压泵输出功率几乎一样;悬臂在下降过程中,采用改进RBF神经网络PID控制方法,液压泵输出功率较小。在空载或轻载工况下,液压起重机采用改进RBF神经网络PID控制方法,能够降低液压泵能耗损失,节约资源。
When a hydraulic crane drives a small load,the output power of the hydraulic pump is large,which results in energy waste.In this regard,an improved radial basis function(RBF)neural network PID control method is proposed,and the output power of the hydraulic pump is simulated.The plane sketch of the hydraulic crane is created,the load sensitive balance valve is designed,and the driving dynamic equation of the hydraulic crane is deduced.The structure of traditional RBF neural network is improved.A dynamic adaptive RBF neural network PID controller is designed.The effect of improved RBF neural network control for hydraulic crane is simulated by using Matlab software.The results show that the output power of hydraulic pump is almost the same when using RBF neural network PID control and improved RBF neural network PID control in the process of cantilever rising under no-load or light-load conditions,and the output power of hydraulic pump is smaller when using improved RBF neural network PID control in the process of cantilever falling.Under no-load or light-load conditions,the hydraulic crane adopts improved RBF neural network PID control method,which can reduce the energy consumption loss of hydraulic pump and save resources.
作者
李锐
崔宇
LI Rui;CUI Yu(School of Mechanical-Electronic and Automobile Engineering,Changzhou Vocational Institute of Engineering,Changzhou 213164,Jiangsu,China;Department of Mechanical and Power Engineering,Yingkou Institute of Technology,Yingkou 115014,Liaoning,China)
出处
《中国工程机械学报》
北大核心
2020年第3期269-273,共5页
Chinese Journal of Construction Machinery
基金
江苏高校“青蓝工程”资助项目。
关键词
液压起重机
改进RBF神经网络
PID控制
节能
仿真
hydraulic crane
improved radial basis function(RBF)neural network
PID control
energy saving
simulation
