Zohar Feldman, Avishai Mandelbaum
WSC 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.
Zohar Feldman, Avishai Mandelbaum
WSC 2010
Thomas R. Puzak, A. Hartstein, et al.
CF 2007
Elliot Linzer, M. Vetterli
Computing
Anupam Gupta, Viswanath Nagarajan, et al.
Operations Research