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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 927905, 6 pages
Research Article

Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

Department of Scholarships and Cultural Relations, Ministry of Higher Education and Scientific Research, Al-Nidhal Street, Baghdad, Iraq

Received 18 May 2012; Accepted 8 September 2012

Academic Editor: Joanna Józefowska

Copyright © 2012 Ali T. Hasan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.