Atomic Force Microscopy Imaging Based on Iterative Learning Control

This paper presents a novel controller design based on iterative learning control (ILC) for atomic force microscopy (AFM) nanopositioning. The controller focuses on eliminating the adverse effects brought by nonlinear of piezoelectric actuator and external environmental interference. Specifically, scanning in the horizontal plane of AFM is regarded as a path tracking control problem and the error information of the previous iteration periods is used to modify the control input, to ensure the fast convergence of the output along the iterative axis. The tracking simulation and AFM imaging experimental results are presented and show that the proposed controller can effectively eliminate the adverse effects and significantly improve the imaging accuracy of AFM.