Strong and flexible domain typing for dynamic E-business
Yigal Hoffner, Simon Field, et al.
EDOC 2004
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.
Yigal Hoffner, Simon Field, et al.
EDOC 2004
J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
György E. Révész
Theoretical Computer Science