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

Author

Baruch, I.S. ; Flores, J.M. ; Garrido, R.

Author_Institution

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

fYear

2001

fDate

4-7 Sept. 2001

Firstpage

3540

Lastpage

3545

Abstract

The paper proposed to apply a fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear plants. The parameters of the local recurrent neural network models are used for a local indirect adaptive trajectory tracking 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 results.

Keywords

adaptive control; control system synthesis; fuzzy control; fuzzy neural nets; neurocontrollers; nonlinear control systems; recurrent neural nets; state estimation; trajectory control; complex nonlinear plants; fuzzy neural recurrent multimodel; fuzzy rule based control system; intelligent control system; local control law design; local indirect adaptive trajectory tracking control system design; local recurrent neural network models; state estimation; systems identification; Adaptation models; Adaptive control; Artificial neural networks; Equations; Mathematical model; System identification; Vectors; Recurrent neural networks; fuzzy-neural multi-model; indirect adaptive control; systems identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 2001 European

Conference_Location

Porto

Print_ISBN

978-3-9524173-6-2

Type

conf

Filename

7076483