Title :
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
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;
Conference_Titel :
Control Conference (ECC), 2001 European
Conference_Location :
Porto
Print_ISBN :
978-3-9524173-6-2
