Iterative Learning Control for robotic deposition using machine vision

This work presents a new application of iterative learning control (ILC) in two respects. Firstly, the output signal is generated by a machine vision system. Secondly, ILC is applied to the extrusion process in micro robotic deposition (muRD), directly addressing the end product quality instead of contributors to end product quality such as position tracking. A P-type and model inversion learning function are both applied to the extrusion process, a system that has nonlinear dynamics and no readily available volumetric flowrate sensor. Theoretical and experimental results show that the nominal system is first order with a pure time delay. Both P-type and model inversion ILC improve the dynamics, with both systems providing better reference tracking. The ILC compensates for the unmodeled nonlinearities, realizing a reduction of RMS error to less than 20% of the initial value for the model inversion approach. Experiments are performed, displaying the ability to extrude precise and seamless closed shapes with the model inversion ILC. This is a necessary requirement for transitioning materials and embedding sensors in multi- material muRD.