Nanomanipulation Using Atomic Force Microscope with Drift Compensation

Qinmin Yang; Jagannathan Sarangapani

doi:10.1109/ACC.2006.1655408

Nanomanipulation Using Atomic Force Microscope with Drift Compensation

Qinmin Yang
, Jagannathan Sarangapani

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

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

Original language	American English
Journal	Proceedings of the American Control Conference, 2006
DOIs	https://doi.org/10.1109/ACC.2006.1655408
State	Published - Jun 1 2006

Keywords

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

Disciplines

Computer Sciences
Electrical and Computer Engineering

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Nanomanipulation Using Atomic Force Microscope with Drift CompensFinal published version, 384 KBLicense: Unspecified

Cite this

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title = "Nanomanipulation Using Atomic Force Microscope with Drift Compensation",

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",

keywords = "Adaptive Control, Atomic Force Microscopy, Closed Loop Systems, Compensation, Control System Analysis, Force Control, Lyapunov Methods, Manipulators, Neurocontrollers, Stability, Substrates",

author = "Qinmin Yang and Jagannathan Sarangapani",

year = "2006",

month = jun,

day = "1",

doi = "10.1109/ACC.2006.1655408",

language = "American English",

journal = "Proceedings of the American Control Conference, 2006",

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T1 - Nanomanipulation Using Atomic Force Microscope with Drift Compensation

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AU - Sarangapani, Jagannathan

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N2 - 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

AB - 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

KW - Adaptive Control

KW - Atomic Force Microscopy

KW - Closed Loop Systems

KW - Compensation

KW - Control System Analysis

KW - Force Control

KW - Lyapunov Methods

KW - Manipulators

KW - Neurocontrollers

KW - Stability

KW - Substrates

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/1751

U2 - 10.1109/ACC.2006.1655408

DO - 10.1109/ACC.2006.1655408

M3 - Article

JO - Proceedings of the American Control Conference, 2006

JF - Proceedings of the American Control Conference, 2006

ER -

Nanomanipulation Using Atomic Force Microscope with Drift Compensation

Abstract

Keywords

Disciplines

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