Time-varying Position Error Compensation of Machine Tools Based on Dynamic Fuzzy Neural Networks

Abstract

A new time-varying position error compensation method for machine tools based on dynamic fuzzy neural networks(D-FNN) is presented to improve the positioning accuracy of numerical control(NC) machine tools.In view of the complexity of influencing factors of NC machine tool positioning accuracy and the difficulty to obtain fuzzy rules,the D-FNN is improved to fit for multiple-input multiple-output system,and also to automatically online identify and generate fuzzy rules.Through measuring the temperature and positioning accuracy of NC machine tool,a NC machine tool time-varying position error prediction model is build on the basis of the improved D-FNN.Then this model is used to compensate the NC machine tool's positioning error,and its effect is compared with the compensation effect of a radial basis function(RBF) neural network model,which shows that the D-FNN model features high accuracy,strong generalization ability and excellent robustness,thus being more suitable for long-time,high-precision real time compensation of NC machine tools.