On robot motion control via adaptive neural networks

S. Puga, J. Moreno-Valenzuela, V. Santibanez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

In this paper, a nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired applied torque, a neural network is used. Then, adaptation laws for the input and output weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the input and output weights of the neural network are showed to be uniformly bounded. The proposed scheme has been experimentally validated in real time in a horizontal two degrees-of-freedom robot Experimental results confirmed the practical feasibility of the proposed adaptive neural network-based controller.

Idioma originalInglés
Título de la publicación alojadaCCE 2012 - 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control
DOI
EstadoPublicada - 2012
Evento2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2012 - Mexico City, México
Duración: 26 sep. 201228 sep. 2012

Serie de la publicación

NombreCCE 2012 - 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control

Conferencia

Conferencia2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2012
País/TerritorioMéxico
CiudadMexico City
Período26/09/1228/09/12

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