Nanoscale Servo System of AFM using Surface Topography Learning with Perfect Tracking Control

Atomic force microscope (AFM) is a device that can measure the surface of the samples on a nano-scale. It is common that controller of AFM is designed by classic control theory. However, authors have already proposed a surface estimate method based on disturbance observer theory in contact mode. In this paper, perfect tracking control (PTC) is proposed for AFM with surface topography learning, which can guarantee that the error between sample and cantilever becomes perfectly zero at every sampling point when the plant has no modeling error. Although both STO and PTC enable high-speed measurement, the robustness of proposed PTC is much higher than STO. The advantage is confirmed by simulations and experiments.