A fuzzy-neural multi-model for mechanical systems identification and control

Author

Baruch, Ieroham S. ; L, Rafael Beltran ; M, Ruben Garrido ; Gortcheva, Elena

Author_Institution

Dept. of Autom. Control, CINVESTAV-IPN, Mexico City, Mexico

Volume

3

fYear

2003

fDate

5-8 Oct. 2003

Firstpage

2113

Abstract

The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with friction. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. The applicability of the proposed intelligent control system is confirmed by simulation and experimental results, where a good convergence of all recurrent neural networks, is obtained.

Keywords

adaptive control; adaptive systems; backpropagation; control system synthesis; convergence; feedforward neural nets; friction; fuzzy neural nets; fuzzy set theory; intelligent control; neurocontrollers; recurrent neural nets; state estimation; adaptive control systems design; control laws; convergence; friction; fuzzy neural recurrent multimodel; fuzzy rule; intelligent control system; mechanical systems control; mechanical systems identification; nonlinear mechanical plants; recurrent neural network models; states estimation; Adaptive control; Control systems; Friction; Fuzzy control; Fuzzy systems; Intelligent control; Mechanical systems; Recurrent neural networks; State estimation; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2003. IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-7952-7

Type

conf

DOI

10.1109/ICSMC.2003.1244196

Filename

1244196