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Abstract
Cross-coupled iterative learning control has previously been applied to contour tracking problems with planar manufacturing robots in which both axes can be characterised as similar systems; having similar dynamics and identical hardware. However, there are many repetitive applications in which dynamically dissimilar systems cooperate to pursue a primary performance objective. This article introduces a novel framework to couple dynamically dissimilar systems while applying iterative learning control, 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 emphasising an underutilised fast system instead of straining a less-capable slow system. The controller is applied to a micro-robotic deposition 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 14% improvement in fabrication-dimensional accuracy with only marginal changes in actuator effort, as compared to independently controlled axes.
Acknowledgements
The authors acknowledge Dr Tay Bee Yen and Dr Lin Wei at the Singapore Institute of Manufacturing Technology (71 Nanyang Drive, Singapore 638075) for their help with the design and manufacture of the Multi-Material Deposition Head. The authors gratefully acknowledge the contribution and support of the NSF Nano-CEMMS Center under award number DMI-0328162 and CMMI-0749028, along with NSF awards DMI 0140466 and CMMI 09-00184 ARRA. This work was supported by the National Science Foundation (NSF) Nano-CEMMS Center under Award DMI 0328162, CMMI 0749028 and NSF Award DMI 0140466, CMMI 09-00184 ARRA.