Fan Jing Meng, Ying Huang, et al.
ICEBE 2007
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.
Fan Jing Meng, Ying Huang, et al.
ICEBE 2007
Sabine Deligne, Ellen Eide, et al.
INTERSPEECH - Eurospeech 2001
Kaoutar El Maghraoui, Gokul Kandiraju, et al.
WOSP/SIPEW 2010
Rajiv Ramaswami, Kumar N. Sivarajan
IEEE/ACM Transactions on Networking