Automated controller tuning for Atomic Force Microscopes using Estimation Based Multiple Model Switched Adaptive Control

Abstract

Atomic Force Microscopes (AFMs) generate topographic images with nanometer resolution and need little or no sample preparation, however typical operation depends on the proper tuning of a PI controller for vertical nanopositioning. Currently these controllers need to be tuned manually by the end user which reduces their ease of use. Here we develop an automated online Proportional Integral (PI) controller tuning procedure for the control of vertical loop using a multiple model adaptive control (MMAC) approach. The approach is suitable for retro-fitting around an existing PI controller. Preliminary experimental results are presented.