A New Rapid-Precision Position Measurement Method for a Linear Motor Mover Based on a 1-D EPCA

Abstract

This paper is dedicated to studying a new position measurement method of a linear motor mover based on machine vision, which provides a novel idea for the detection of mover position. A rapid-precise measurement system based on a fence image and a phase correlation algorithm (PCA) is established. Multiline sampling and parallel computing methods are used to simplify the fence image directly; these methods can convert the 2-D image motion estimation into a simpler, 1-D signal process. To rapidly detect the mover position with high precision, a 1-D extended phase correlation algorithm (EPCA) is proposed. During position measurement, three key steps for the implementation are applied as follows. 1) Capture objective fence stripes using a high-speed camera installed beside the mover; 2) suppress the spectral leakage of discrete Fourier transform by a Hanning window function; and 3) calculate the displacement vector of the linear motor mover using a 1-D PCA and the least square method. Finally, the actual displacement of the mover can be obtained according to the system calibration. Experimental results demonstrate that the proposed 1-D EPCA implemented at measuring mover position is strongly robust and speedy with a detecting accuracy of 0.02 mm.