Model-free Multi-variable Learning Control of a Five Axis Nanopositioning Stage

This article compares the performance of recently introduced learning control methods on a 5-axis nanopositioning stage. Of these methods, the Smoothed Model-Free Inversion-based Iterative Control (SMF-IIC) method requires no modeling effort for effective tracking of repetitive trajectories and is readily applicable to multi-variable systems. Experimental results show that the tracking performance of the SMF-IIC method is similar to traditional learning control methods when applied to a single axis of the nanopositioning stage. The SMF-IIC method is also found to be effective for reference tracking of two axes simultaneously.