Control of a microscale deposition robot using a new adaptive time-frequency filtered iterative learning control

• Published in: 2004 American Control Conference Proceedings; Volume 6 of 6 Vol. 6; pp. 5144 - 5149 vol.6
• Main Authors: Bristow, D.A., Alleyne, A.G., Danian Zheng
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Piscataway NJ IEEE 01.01.2004; Evanston IL American Automatic Control Council
• Subjects: Adaptative systems; Adaptive control; Adaptive filters; Applied sciences; Computer science; control theory; systems; Control theory. Systems; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; Manufacturing processes; Programmable control; Robotics; Robots; Robustness; Signal and communications theory; Signal, noise; Telecommunications and information theory; Time frequency analysis; Time varying systems; Trajectory; Uncertainty; Adaptive algorithm; Time frequency domain method; Adaptive filter; Modeling; Frequency control; Robotics; Deposition process; Time varying system; Minimum time; Uncertain system; Bandwidth; Robustness; Frequency filtering; Iterative learning control; Intelligent control; Production process
• ISBN: 9780780383357; 0780383354
• ISSN: 0743-1619; 2378-5861
• DOI: 10.23919/ACC.2004.1384668

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.