Classifier-Based Approximator for Friction Compensation in High Accelerated Positioning System

Abstract

This article proposes a classifier-based approximator to compensate for friction in a high accelerated positioning system. Since friction function is globally non-smooth, an unsupervised k-means clustering algorithm is adopted to classify the friction into micro and macro motion segment, then the frictions are estimated with two sub-approximator in the corresponding segment respectively. Due to the unsupervised classification of friction, the classifier-based approximator can realize universal approximation of nonlinear friction with high precision. Finally, comparative experiments on a high accelerated position system driven by voice coil motors are conducted to verify the effectiveness of the proposed method. The proposed method can reduce the root mean square error (RMSE) of tracking by 57.2%, 19.1%, and 27.4% compared with a parametric model, recurrent neural network (RNN), and incremental extreme learning machine (I-ELM), respectively.