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.