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Micro-force compensation in automated micro-object positioning using adaptive neural networks

M Shahini1, W W Melek1,3 and J T W Yeow2

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This paper proposes a novel approach for controlled pushing of a micro-sized object along a desired path. Challenges associated with this control task due to the presence of dominating micro-forces are carefully studied and a solution based on the application of artificial neural networks is introduced. A nonlinear controller is proposed for controlled pushing of micro-objects which guarantees the stability of the closed-loop system in the Lyapunov sense. An experimental setup is designed to validate the performance of the proposed controller. Results suggest that artificial neural networks present a promising tool for design of adaptive controllers to accurately manipulate objects in the microscopic scale.


PACS

07.05.Mh Neural networks, fuzzy logic, artificial intelligence

87.18.Sn Neural networks and synaptic communication

07.10.Cm Micromechanical devices and systems

85.85.+j Micro- and nano-electromechanical systems (MEMS/NEMS) and devices

Subjects

Electronics and devices

Instrumentation and measurement

Biological physics

Nanoscale science and low-D systems

Dates

Issue 9 (September 2009)

Received 8 April 2009

Published 17 July 2009



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