In my application, I am attempting to control the contact force of an end effector. I am getting very poor performance for two primary reasons: the feedback signal has 1) internal delay and it is significantly 2) undersampled.
Unfortunately I am very limited and I cannot improve on the delay of the feedback signal, however I believe there are methods on improve the sampling rate.
I have heard that you can use a Kalman filter in the feedback line to extrapolate in real-time and predict feedback information and improve on sampling. Before adventuring into this, I was wondering if someone can confirm that you can use a Kalman filter to do this. If not, what other methods can be used. Internal modeling is difficult because this system is dominated by external disturbances.
Once the data is undersampled, the high freq information is gone (best case), and if you haven't prefiltered prior to sampling, it is aliased down to a lower frequency.
It can't be undone. A Kalman filter or any interpolation algorithm will not recover it.
If you can tolerate a sample of delay, you can probably upsample the data sufficiently to control your system, but if the poor control is because you're missing high frequency info, or because the high freqs are aliased, you're out of luck.
Your only real hope is that the data is not undersampled, and you're having poor performance for some other reason. In this case, a predictive filter may be of help, as might some other stuff. We'd need some detail to try to figure this out. Can you share why you think the data is undersampled?