光学精密工程2017，Vol.25Issue(5)：1266-1271,6.DOI:10.3788/OPE.20172505.1266
基于径向基函数神经网络的压电式六维力传感器解耦算法
Decoupling algorithms for piezoelectric six-dimensional force sensor based on RBF neural network
摘要
Abstract
For problems of poor linearity and too many inter-dimensional coupling errors of a four-point supporting piezoelectric six-dimensional force sensor, the decoupling algorithms based on Redial Basis Function (RBF) neural network were proposed.Main factors to produce coupling errors were analyzed and the RBF neural network was established.The six-dimensional force sensor was calibrated experimentally to obtain experimental data for decoupling, and the data were processed by the nonlinear decoupling algorithm based on RBF neural network.Then the mapping relation between input and output was acquired by decoupling and the decoupled data from the sensor was obtained.These data were analyzed, and the result shows that the biggest classⅠerror and classⅡerror by the proposed nonlinear decoupling algorithm based on RBF neural network are 1.29% and 1.56% respectively.The experimental analysis shows that it will effectively reduce the classⅠerrors and the classⅡerrors through nonlinear decoupling algorithm based on RBF neural network, and meets the requirements that the two kinds of error indicators of the sensor should be less than 2%.The proposed algorithm improves the measuring accuracy of sensors and overcomes the difficulty on decoupling.关键词
六维力传感器/压电式传感器/径向基函数神经网络/解耦算法Key words
six-dimensional force sensor/piezoelectric sensor/Radial Basis Function(RBF) neural network/decoupling algorithm分类
信息技术与安全科学引用本文复制引用
李映君,韩彬彬,王桂从,黄舒,孙杨,杨雪,陈乃建..基于径向基函数神经网络的压电式六维力传感器解耦算法[J].光学精密工程,2017,25(5):1266-1271,6.基金项目
国家自然科学基金资助项目(No.51205165) （No.51205165）
山东省自然科学基金联合专项资助项目(No.ZR2015EL031) （No.ZR2015EL031）
山东省教育厅科技发展计划资助项目(No.TJY1405) （No.TJY1405）
