Kaoutar El Maghraoui, Gokul Kandiraju, et al.
WOSP/SIPEW 2010
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.
Kaoutar El Maghraoui, Gokul Kandiraju, et al.
WOSP/SIPEW 2010
A. Gupta, R. Gross, et al.
SPIE Advances in Semiconductors and Superconductors 1990
G. Ramalingam
Theoretical Computer Science
Charles H. Bennett, Aram W. Harrow, et al.
IEEE Trans. Inf. Theory