基于RBF神经网络的混凝土泵车臂架运动学逆解

The inverse kinematics solution for boom of concrete pump vehicles based on the RBF network

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°.