Nanomanipulation Using Atomic Force Microscope with Drift Compensation
This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1751
There were 12 downloads as of 28 Jun 2016.
Abstract
This paper proposes an atomic force microscope (AFM) based force controller to push nanoparticles on the substrates since it is tedious for human. A block phase correlation-based algorithm is embedded into the controller for compensating the thermal drift during nanomanipulation. Further, a neural network (NN) is employed to approximate the unknown nanoparticle and substrate contact dynamics including the roughness effects. Using the NN-based adaptive force controller the task of pushing nanoparticles is demonstrated. Finally, using the Lyapunov-based stability analysis, the uniform ultimately boundedness (UUB) of the closed-loop signals is demonstrated
