Continuous-Time Neural Identification for a 2 DOF Vertical Robot Manipulator

Author

Jurado, Francisco ; Flores, María A. ; Santibañez, V. ; Llama, M.A. ; Castañeda, Carlos E.

Author_Institution

Div. de Estudios de Posgrado e Investig., Inst. Tecnoldgico de la Laguna, Coahuila de Zaragoza, Mexico

fYear

2011

fDate

15-18 Nov. 2011

Firstpage

77

Lastpage

82

Abstract

A Recurrent High-Order Neural Network (RHONN) structure as well as a decentralized neural network scheme, this latter with high-order interconnections, are proposed to execute continuous-time identification of a two degrees of freedom (DOF) direct drive vertical planar robot manipulator model, on which effects due to friction and gravity forces are both considered. The neural network learning is achieved online using the filtered error approach. The performance of both neural networks schemes is illustrated via simulation results.

Keywords

continuous time systems; drives; friction; learning systems; manipulators; multivariable systems; neurocontrollers; recurrent neural nets; 2 DOF vertical robot manipulator; continuous-time neural identification; decentralized neural network scheme; direct drive vertical planar robot manipulator model; filtered error approach; friction; gravity forces; high-order interconnection; neural network learning; recurrent high-order neural network structure; two degrees of freedom; Approximation methods; Biological neural networks; Joints; Manipulators; Neurons; Vectors; filtered error; neural identification; recurrent high-order neural network; robot manipulator;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2011 IEEE

Conference_Location

Cuernavaca, Morelos

Print_ISBN

978-1-4577-1879-3

Type

conf

DOI

10.1109/CERMA.2011.20

Filename

6125802