On robot motion control via adaptive neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationCCE 2012 - 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control
DOIs
StatePublished - 2012
Event2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2012 - Mexico City, Mexico
Duration: 26 Sep 201228 Sep 2012

Publication series

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

Conference

Conference2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2012
Country/TerritoryMexico
CityMexico City
Period26/09/1228/09/12

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