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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 451049, 12 pages
Research Article

Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network

1Tecnológico Nacional de México, Instituto Tecnológico de La Laguna, Boulevard Revolución y Calzada Cuauhtémoc, 27000 Torreón, COAH, Mexico
2Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Boulevard Marcelino García Barragán No. 1421, 44430 Guadalajara, JAL, Mexico

Received 27 October 2014; Revised 7 March 2015; Accepted 10 March 2015

Academic Editor: Fernando Torres

Copyright © 2015 Luis A. Vázquez 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.


A decentralized recurrent wavelet first-order neural network (RWFONN) structure is presented. The use of a wavelet Morlet activation function allows proposing a neural structure in continuous time of a single layer and a single neuron in order to identify online in a series-parallel configuration, using the filtered error (FE) training algorithm, the dynamics behavior of each joint for a two-degree-of-freedom (DOF) vertical robot manipulator, whose parameters such as friction and inertia are unknown. Based on the RWFONN subsystem, a decentralized neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via real-time results.