Control of a microscale deposition robot using a new adaptive time-frequency filtered iterative learning control
A robocasting manufacturing process and robotic deposition machine are presented in this paper. The process requires that the machine be able to track 3-D trajectories with high precision. Iterative learning control (ILC) is presented as a viable strategy to meet these demands. Typically, practical...
Saved in:
Published in | 2004 American Control Conference Proceedings; Volume 6 of 6 Vol. 6; pp. 5144 - 5149 vol.6 |
---|---|
Main Authors | , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
Piscataway NJ
IEEE
01.01.2004
Evanston IL American Automatic Control Council |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | A robocasting manufacturing process and robotic deposition machine are presented in this paper. The process requires that the machine be able to track 3-D trajectories with high precision. Iterative learning control (ILC) is presented as a viable strategy to meet these demands. Typically, practical implementation of ILC requires some type of Q-filtering that creates an inherent tradeoff between performance and robustness. This tradeoff can be minimized by using a time-varying Q-filter that has been tailored to the system and reference trajectory. A new adaptive time-frequency Q-filtered ILC algorithm is presented to adaptively construct a tailored time-varying Q-filter. Further, because the approach is adaptive, the performance is not limited by overly conservative uncertainty models. A simulation example is presented to demonstrate that, when designed for a nominal plant, the adaptive Q-filtered ILC has performance comparable to that of a standard, fixed-bandwidth Q-filtered ILC. When a perturbation of the plant is introduced, the adaptive Q-filtered ILC adapts to maintain stability, whereas the fixed-bandwidth Q-filtered ILC becomes unstable. The adaptive algorithm is applied to the robotic deposition machine to demonstrate the ability of the algorithm to achieve high precision in this application. |
---|---|
Bibliography: | SourceType-Scholarly Journals-2 ObjectType-Feature-2 ObjectType-Conference Paper-1 content type line 23 SourceType-Conference Papers & Proceedings-1 ObjectType-Article-3 |
ISBN: | 9780780383357 0780383354 |
ISSN: | 0743-1619 2378-5861 |
DOI: | 10.23919/ACC.2004.1384668 |