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Journal of Control Science and Engineering
Volume 2012, Article ID 761019, 11 pages
http://dx.doi.org/10.1155/2012/761019
Research Article

Dynamics Model Abstraction Scheme Using Radial Basis Functions

1Department of Computer Architecture and Technology, CITIC ETSI Informática y de Telecomunicación, University of Granada, Spain
2PSPC Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genoa, Italy

Received 27 July 2011; Accepted 24 January 2012

Academic Editor: Wen Yu

Copyright © 2012 Silvia Tolu et al. 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.

Abstract

This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.