Conference paper
Performance measurement and data base design
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
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.
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
Raymond F. Boyce, Donald D. Chamberlin, et al.
CACM
Xiaozhu Kang, Hui Zhang, et al.
ICWS 2008
Khaled A.S. Abdel-Ghaffar
IEEE Trans. Inf. Theory