Nanoscale servo control of contact-mode AFM with surface topography learning observer

Atomic force microscope (AFM) is a device that can measure the surface of the samples on a nano-scale. Most of the controllers of commercial AFMs are designed by classic control theory. However, sophisticated control theory has been applied in recent academic papers. Authors have already proposed a surface topography observer (STO) based on disturbance observer theory in contact mode. In this paper, perfect tracking control (PTC) is applied to contact-mode AFM with surface topography learning. PTC can guarantee that the error between the plant output and the desired trajectory becomes perfectly zero at every sampling point when the plant has no modeling error. Moreover, a surface topography learning observer (STLO) is proposed to generate feedforward compensation signal based on STO. These three methods are compared in simulations and experiments.