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Robust Integral of NN and Error Sign Control for Nanomanipulation Using AFM

Robust Integral of NN and Error Sign Control for Nanomanipulation Using AFM

Qinmin Yang, Jiangang Lu
Copyright: © 2012 |Volume: 2 |Issue: 2 |Pages: 13
ISSN: 2156-1664|EISSN: 2156-1656|EISBN13: 9781466613041|DOI: 10.4018/ijimr.2012040106
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MLA

Yang, Qinmin, and Jiangang Lu. "Robust Integral of NN and Error Sign Control for Nanomanipulation Using AFM." IJIMR vol.2, no.2 2012: pp.78-90. http://doi.org/10.4018/ijimr.2012040106

APA

Yang, Q. & Lu, J. (2012). Robust Integral of NN and Error Sign Control for Nanomanipulation Using AFM. International Journal of Intelligent Mechatronics and Robotics (IJIMR), 2(2), 78-90. http://doi.org/10.4018/ijimr.2012040106

Chicago

Yang, Qinmin, and Jiangang Lu. "Robust Integral of NN and Error Sign Control for Nanomanipulation Using AFM," International Journal of Intelligent Mechatronics and Robotics (IJIMR) 2, no.2: 78-90. http://doi.org/10.4018/ijimr.2012040106

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Abstract

This paper presents a novel control methodology for automatically manipulating nano particles on the substrate by using Atomic Force Microscope (AFM). The interactive forces and dynamics between the tip, particle and substrate are modeled and analyzed including the roughness effect of the substrate. Further, the control signal is designed to consist of the robust integral of a neural network (NN) output plus the sign of the error feedback signal multiplied with an adaptive gain. Using the NN-based adaptive force controller, the task of pushing nano particles is demonstrated in simulation environment. Finally, the asymptotical tracking performance of the closed-loop system, boundedness of the NN weight estimates and applied forces are shown by using the Lyapunov-based stability analysis.

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