Vision Based Iterative Learning Control of a MEMS Micropositioning Stage with Intersample Estimation and Adaptive Model Correction

In this work the use of an Iterative Learning Control (ILC) algorithm to precisely control a highly nonlinear Micro-Electro-Mechanical (MEMS) micropositioning stage is demonstrated. Vision-based feedback with low sampling rate is augmented with estimates from a Kalman Filter to generate a high sampling rate estimate of the output. Nonlinearities in the system are accounted for using a linear parameter varying model based on experimental results. An automatic model correction technique based on measurement residual is also presented that increases the final estimation accuracy by over 70 percent. The effectiveness of the approach is demonstrated by tracking a 4 Hz sinusoid using 10 Hz camera feedback with a resulting RMS error of 0.25 micrometers.