Precise Position Measurement of Linear Motor Mover Based on Multiframe Data Fusion and Extended Uncertainty

The cumulative measurement error caused by the incremental position detection method is a key factor affecting the position detection accuracy of the linear motor mover. This work presents an image-based position measurement method based on the multiframe data fusion (MFDF) algorithm and extended uncertainty (EU) optimization to suppress cumulative measurement errors of the linear motor mover. First, based on the image position measurement model of the linear motor and the requirements of displacement measurement, a theoretical analysis was conducted on the transfer formula of the cumulative measurement error of the linear motor displacement. Second, the error distribution laws of different adjacent frame algorithms are discussed, and a MFDF method is proposed, which can construct multiple measurement processes at each displacement feedback point, to reduce the random error in the cumulative error. Subsequently, the EU optimization index is adopted to optimize the selection of the adjacent frame algorithm, further reduce the systematic error in the cumulative error, and achieve high precision measurement of the linear motor mover position. Finally, a linear motor mover position measurement system was established, and some prototype experiments for linear motor displacement measurement were carried out. The experimental results show that this method can effectively suppress cumulative errors in the incremental position detection and achieve accurate feedback on the mover position. It also exhibits higher accuracy and interference resistance under different working conditions.