Accumulated Error Reduction of Linear Motor Mover Position Measurement Based on SKLM

Abstract

Cumulative measurement error is the most critical factor affecting the accuracy of long-stroke displacement measurement. Based on the one-dimensional image gradient method and Smooth Kalman filter algorithm (SKLM), this paper proposes a long-stroke linear motor displacement cumulative error reduction and high precision measurement methods. First, according to the motion characteristics of the linear motor and the principle of image measurement, a one-dimensional speckle target image is generated, and the one-dimensional Barron gradient algorithm is used to calculate the displacement of adjacent frames quickly. Second, according to the rea-sons for the accumulated error of long-stroke displacement measurement, the smooth Kalman algorithm for optimized autoregressive data processing is introduced to optimally estimate the measured displacement of adjacent frames to realize the reduction of accumulated error. To improve the robustness of the measurement system, a wavelet soft threshold image filter is introduced to per-form noise reduction and restoration processing on the collected signal, and further realize the high-precision dis-placement measurement of the long-stroke linear motor. Simulations and experiments show that the method in this paper not only improves the measurement accuracy of adjacent frames, but also reduces the cumulative error of long-stroke displacement measurement. And under different working conditions, compared with other methods, it has higher accuracy and anti-interference.