Title :
Design of a feedforward controller for AFM nanopositioning based on neural network control theory
Author :
Payam, Amir Farrokh ; Yazdanpanah, Mohammad Javad ; Fathipour, Morteza
Author_Institution :
Device Modelling & Simulation Lab., Univ. of Tehran, Tehran
Abstract :
This paper presents design procedure for a neural feedforward controller which can be used as an atomic force microscope system. We have used a three layered feed forward neural network for designing Feedforward Controller with Plant Inverse Learning. The effectiveness and validity of the designed controller were investigated by computer simulation and results obtained are compared with other control methods, and show superior performance. Advantages of using proposed controller include increased bandwidth of operation and easy implementation in nanopositioning for the AFM.
Keywords :
atomic force microscopy; controllers; nanopositioning; neural nets; AFM nanopositioning; atomic force microscope system; computer simulation; feedforward controller; neural network control theory; plant inverse learning; Atomic force microscopy; Atomic layer deposition; Computer simulation; Control systems; Control theory; Feedforward neural networks; Feeds; Force control; Nanopositioning; Neural networks; AFM control; Atomic Force Microscope; Nanopositioning; Piezoelectric Actuator;
Conference_Titel :
Nano/Micro Engineered and Molecular Systems, 2009. NEMS 2009. 4th IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-4629-2
Electronic_ISBN :
978-1-4244-4630-8
DOI :
10.1109/NEMS.2009.5068679
