Observer design for a nano-positioning system using neural, fuzzy and ANFIS networks

Abstract

This paper focuses on the observer design for a 2D nano-positioner. In order to position the stage with a desired accuracy, it is required to adjust the stage displacements with a closed-loop control system. Since displacement and velocity of the main stage are not measured directly in the designed nano-positioning system, some observers should be designed to estimate these state variables using data provided by measurable variables. To this end, three different observers were designed based on neural, fuzzy and adaptive neuro fuzzy inference system (ANFIS) networks. With the purpose of obtaining data for training the observer model, a reference model is required. For this reason, the mechanism was modelled in COMSOL. The results show that among the designed observers, ANFIS can estimate the system states with higher accuracy compared to the two other observers. Currently, the position of the stage in the commercial XY nano-positioners is measured using two relatively high-cost capacity sensors that need driver circuits, while based on the observation scheme proposed in this paper, the complexity and cost of the nano-positioning systems can be reduced by using strain gauge type sensors mounted on piezo-actuators.