Marshall W. Bern, Howard J. Karloff, et al.
Theoretical Computer Science
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.
Marshall W. Bern, Howard J. Karloff, et al.
Theoretical Computer Science
Indranil R. Bardhan, Sugato Bagchi, et al.
JMIS
Matthias Kaiserswerth
IEEE/ACM Transactions on Networking
Fan Zhang, Junwei Cao, et al.
IEEE TETC