Conference paper
Global routing revisited
Michael D. Moffitt
ICCAD 2009
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.
Michael D. Moffitt
ICCAD 2009
B. Wagle
EJOR
Leo Liberti, James Ostrowski
Journal of Global Optimization
Maciel Zortea, Miguel Paredes, et al.
IGARSS 2021