Abstract
A Robocasting manufacturing process and robotic deposition machine are presented. 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.
Original language | English (US) |
---|---|
Pages (from-to) | 5144-5149 |
Number of pages | 6 |
Journal | Proceedings of the American Control Conference |
Volume | 6 |
State | Published - Nov 29 2004 |
Event | Proceedings of the 2004 American Control Conference (AAC) - Boston, MA, United States Duration: Jun 30 2004 → Jul 2 2004 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering