考虑动力变动关系的液压柔性机械臂末端定位控制方法  

Hydraulic Flexible Robotic Arm End Positioning Control Method Considering Power Variation Relationship

作　　者：丁心安 李美莹 DING Xin’an;LI Meiying(School of Institute of Technology,Xi’an Siyuan University,Xi’an 710038,China)

机构地区：[1]西安思源学院理工学院,西安710038

出　　处：《自动化与仪表》2024年第12期47-50,55,共5页Automation & Instrumentation

摘　　要：液压柔性机械臂受摩擦力、重力以及牵引力等多种动力势能影响,导致运动特征的变动关系复杂,定位控制误差较大。为此,提出一种基于RBF神经网络的末端定位控制方法。建立RBF神经网络架构,采用高斯函数作为输入层和模糊层的连接函数,以摩擦力、牵引力、重力等值作为导入因子,分析其动力学特性。以此为约束,设计非线性的定位控制补偿方法,通过多次迭代调整平衡阈值实现高质量的定位控制。实验结果证明,所提方法定位控制精准度较高,控制后的机械臂运动旋转角度以及垂直角度曲线变化与目标值基本一致,控制效果表现优异。The hydraulic flexible robotic arm is affected by various dynamic potential energies such as friction,gravity,and traction,resulting in complex changes in motion characteristics and significant positioning control errors.Therefore,a terminal positioning control method based on RBF neural network is proposed.Establish an RBF neural network architecture,using Gaussian functions as the connection function between the input layer and the fuzzy layer,and introducing factors such as friction,traction,and gravity to analyze its dynamic characteristics.Based on this constraint,a nonlinear positioning control compensation method is designed to achieve high-quality positioning control by adjusting the balance threshold through multiple iterations.The experimental results demonstrate that the proposed method has high accuracy in positioning control,and the changes in the motion rotation angle and vertical angle curve of the controlled robotic arm are basically consistent with the target values,demonstrating excellent control performance.

关 键 词：RBF神经网络 液压柔性机械臂 末端定位控制 高斯函数 势能因子 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]