Can Kalman filter be used to increase sampling rate of a real-time information?
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?