Rolf Clauberg
IBM J. Res. Dev
This paper presents a learning self-tuning (LSTR) regulator which improves the tracking performance of itself while performing repetitive tasks. The controller is a self-tuning regulator based on learning parameter estimation. Experimentally, the controller was used to control the movement of a nonlinear piezoelectric actuator which is a part of the tool positioning system for a diamond turning lathe. Experimental results show that the controller is able to reduce the tracking error through the repetition of the task. © 1993 by ASME.
Rolf Clauberg
IBM J. Res. Dev
J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998
Matthias Kaiserswerth
IEEE/ACM Transactions on Networking
S.F. Fan, W.B. Yun, et al.
Proceedings of SPIE 1989