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 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.

Meeting Name

American Control Conference, 2004


Mechanical and Aerospace Engineering

Keywords and Phrases

Adaptive Control; Casting; Industrial Robots; Iterative Methods; Learning Systems; Position Control; Robust Control; Time-Frequency Analysis

Document Type

Article - Conference proceedings

Document Version


File Type





© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2004

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