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A Neural Network-Based Controller for a Piezoelectrically-Actuated Nano/Micromanipulator

[+] Author Affiliations
Sohrab Eslami, Nader Jalili, Reza Saeidpourazar

Clemson University, Clemson, SC

Paper No. SMASIS2008-630, pp. 539-546; 8 pages
doi:10.1115/SMASIS2008-630
From:
  • ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2
  • Ellicott City, Maryland, USA, October 28–30, 2008
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-4332-1 | eISBN: 978-0-7918-3839-6
  • Copyright © 2008 by ASME

abstract

Nowadays, nanorobots form the most important element of many precision manufacturing and industries such as medical instruments. Apparently, the most important property of these devices is their precision and durability. The more they have accuracy, the more delicate tasks may be performed. Hence, the design of an accurate model for achieving this objective is a challenging problem that has attracted the attention of many recent researchers in the nanotechnology field. In this paper, the kinematics and dynamics as well as development of appropriate controllers for a RRP (revolute-revolute-prismatic) nanomanipulator are presented. The specific task considered here for this manipulator is for the tip to track a predefined trajectory. For the real-time control implementation, a neural network (NN) is applied in order for the fast response of the system. A comparison between a standard PD controller and NN-based controller is presented through a numerical example.

Copyright © 2008 by ASME

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