基于RBF神经网络的混凝土泵车臂架运动学逆解

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英文篇名：The inverse kinematics solution for boom of concrete pump vehicles based on the RBF network 作者：余真珠 ; 王晓明 英文作者：Yu Zhenzhu;Wang Xiaoming;College of Mechanical and Electrical Engineering,Qingdao University of Science & Technology; 关键词：混凝土泵车臂架 ; 运动学逆解 ; 径向基函数神经网络 ; 正交最小二乘法 英文关键词：boom of concrete pump vehicles;;inverse kinematics;;the RBF neural network;;the Orthogonal Least Squares 中文刊名：JXZZ 英文刊名：Machine Design and Manufacturing Engineering 机构：青岛科技大学机电工程学院; 出版日期：2019-01-15 出版单位：机械设计与制造工程 年：2019 期：v.48;No.422 语种：中文; 页：JXZZ201901004 页数：5 CN：01 ISSN：32-1838/TH 分类号：18-22

摘要

针对多自由度、高柔性的混凝土泵车臂架运动学逆解不唯一的问题,利用RBF神经网络来求解其逆解。在RBF神经网络的训练中合理利用正交最小二乘法,使得构建的RBF神经网络快速稳定。经MATLAB仿真分析表明,该方法计算量小且精度高,能将各臂节旋转误差控制在0.5°以内。
        Aiming at the unique inverse kinematics solution for boom of concrete pump vehicles with the multi-degree and higher flexibility,the RBF neural network is used to solve the inverse solution,the Orthogonal Least Squares(QLS) method is used rationally,which makes the RBF neural network fast and stable.Finally,it is manifest that the method has a small amount of calculation and higher precision with using MATLAB for simulation analysis,which the angle of rotation can be reduced in the range of 0.5°.

引文

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