Decreasing the minimum feature size of solid free-form (SFF) fabrication techniques requires advancements in both the SFF process and the actuating hardware. Microscale robotic deposition (mu-RD) is an ink-deposition SFF process where recent advances in ink design coupled with a high-precision motion system can lead to the fabrication of parts with microscale-sized features. This paper presents a control algorithm that combines nonlinearity compensation and a learning feedforward approach to achieve high-precision tracking with a standard, off-the-shelf motion system. The off-the-shelf motion system is affected by several nonlinear disturbances that severely inhibit the accuracy of linear models for small motions. Iterative learning control (ILC) is used in an inverse identification procedure to obtain accurate maps of the disturbances. These maps are used in the controller to yield a linear system after nonlinearity cancellation. As a further improvement, ILC is used to increase accuracy in tracking the repetitive portion of specific part trajectories. The combined approach yields extremely low contour tracking errors and is used to fabricate two types of periodic parts demonstrating high aspect ratios and spanning elements. Although high-precision tracking can also be achieved with an expensive, customized system, the off-the-shelf system combined with the control technique presented here provides a more cost-effective solution. The proposed control technique is effective for improving performance of repeatable, but uncertain nonlinear systems
['Douglas A. Bristow', 'Andrew G. Alleyne']
A High Precision Motion Control System With Application to Microscale Robotic Deposition