# Artificial Neural Network-based error compensation procedure for low-cost encoders

## TL;DR

This paper presents an ANN-based method to significantly improve the accuracy of resolver-based 16-bit encoders by compensating for their systematic error profiles, validated through experiments on multiple encoders.

## Contribution

The paper introduces a neural network error compensation technique tailored for individual encoders, achieving nearly tenfold accuracy improvement over standard encoder performance.

## Key findings

- Encoder accuracy improved from ~6 arc-min to ~0.65 arc-min.
- Error profiles are reproducible over time, validating systematic error correction.
- Method successfully applied to four different encoders.

## Abstract

An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.