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

Meeting Name

American Control Conference, 2006

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Lyapunov Methods; Adaptive Control; Atomic Force Microscopy; Closed Loop Systems; Compensation; Control System Analysis; Force Control; Manipulators; Neurocontrollers; Stability; Substrates

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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