六维力传感器静态解耦方法

摘要

在系统分析六维力传感器线性解耦基本原理的基础上,依据各向同性指标比较分析了基于克拉默法则和最小二来法线性解耦算法的优劣,建立了RBF神经网络非线性解耦模型.研究结果表明:与克拉默法则相比,最小二乘法得到的标定矩阵具有更优的各向同性；以径向基神经网络逼近广义力向量和输出电压之间的函数关系可大大减小六维力传感器的线性误差和维间耦合,其总体误差低于1％FS.%This paper analyzed the fundamental principle of the linear decoupling of the six-ais force/ torque sensor, and according to the index of isotropy, compared two different linear decoupling algorithms, which were based on the Cramer theorem and the least square method and established nonlinear decoupling method of radial basis function neural network. The research result indicates that the isotropy of calibrated matrix gained by the least square method is superior to the one by Cramer theorem and that the linear error and dimensional interference are greatly reduced by using the RBF neural network model to approach the function relationship between generalized force vector and output voltage, with the overall error less than 1% full scale.