Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
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.
Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
Thomas R. Puzak, A. Hartstein, et al.
CF 2007
Ruixiong Tian, Zhe Xiang, et al.
Qinghua Daxue Xuebao/Journal of Tsinghua University
Raymond F. Boyce, Donald D. Chamberlin, et al.
CACM