Table of Contents
ISRN Robotics
Volume 2013, Article ID 173703, 11 pages
Research Article

Comparative Study between Robust Control of Robotic Manipulators by Static and Dynamic Neural Networks

1National Institute of Applied Science and Technology (INSAT), Northern Urban Center Mailbox 676, 1080 Tunis, Tunisia
2Department of Physics and Electrical Engineering, National Institute of Applied Science and Technology (INSAT), Tunisia

Received 28 January 2013; Accepted 20 March 2013

Academic Editors: A. Bechar, A. Sabanovic, R. Safaric, K. Terashima, and C.-C. Tsai

Copyright © 2013 Nadya Ghrab and Hichem Kallel. 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.


A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dynamic neural network for the identification of the robot state. The neural network weight tuning algorithms, for the two approaches, are developed based on Lyapunov theory. Simulation results show that the system response, equipped by dynamic neural network controller, has better tracking performance, has faster response time, and is more reliable to face disturbances and robotic uncertainties.