Conference paper
Optimization algorithms for energy-efficient data centers
Hendrik F. Hamann
InterPACK 2013
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.
Hendrik F. Hamann
InterPACK 2013
Elliot Linzer, M. Vetterli
Computing
Limin Hu
IEEE/ACM Transactions on Networking
Robert E. Donovan
INTERSPEECH - Eurospeech 2001