Cross Coupled Iterative Learning Control of Dissimilar Dynamical Systems

Abstract

Cross Coupled Iterative Learning Control (CCILC) has previously been applied to contour tracking problems with planar robots in which both axes can be characterized as similar systems; having similar dynamics and identical hardware. However, there are many repetitive applications in which dissimilar systems cooperate to pursue a primary performance objective. This paper introduces a novel framework to couple dissimilar systems while applying Iterative Learning Control (ILC), showing the ability to noncausally compensate for a slow system with a fast system. In this framework, performance requirements for a primary objective can more readily be achieved by emphasizing an underutilized fast system instead of straining a less-capable slow system. The controller is applied in simulation and experimentally to a micro-Robotic Deposition (μRD) manufacturing system to coordinate a slow extrusion system axis and a fast positioning system axis to pursue the primary performance objective, dimensional accuracy of a fabricated part. Experimental results show a 30% improvement in fabrication dimensional accuracy with only marginal changes in actuator effort in the slow system, as compared to independently controlled axes.