Continuous-time decentralized wavelet neural control for a 2 DOF robot manipulator

Author

Vazquez, Luis A. ; Jurado, Francisco

Author_Institution

Inst. Tecnol. de la Laguna, Torreón, Mexico

fYear

2014

fDate

Sept. 29 2014-Oct. 3 2014

Firstpage

1

Lastpage

6

Abstract

This paper presents a decentralized wavelet neural control scheme for trajectory tracking of a two degrees of freedom (DOF) vertical robot manipulator. A decentralized recurrent wavelet first order neural network (RWFONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error (FE) training algorithm, the dynamics behavior of the plant. Based on the RWFONN subsystem, a local neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via simulation.

Keywords

continuous time systems; control system synthesis; decentralised control; manipulators; neurocontrollers; trajectory control; wavelet neural nets; FE training algorithm; RWFONN structure; backstepping approach; continuous-time decentralized wavelet neural control; decentralized recurrent wavelet first order neural network; degrees-of-freedom; filtered error training algorithm; neural controller design; series-parallel configuration; two-DOF vertical robot manipulator; Continuous wavelet transforms; Manipulators; Multiresolution analysis; Neural networks; Neurons; Trajectory; Backstepping; decentralized control; filtered error; recurrent neural networks; robot manipulator; trajectory tracking; wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering, Computing Science and Automatic Control (CCE), 2014 11th International Conference on

Conference_Location

Campeche

Print_ISBN

978-1-4799-6228-0

Type

conf

DOI

10.1109/ICEEE.2014.6978295

Filename

6978295