  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.
