An indirect adaptive neural control of nonlinear plants

Author

Baruch, Ieroham ; Albino, José Martín Flores ; Garrido, Ruben ; Gortcheva, Elena

Author_Institution

CINVESTAV-IPN, Mexico City, Mexico

Volume

4

fYear

2000

fDate

2000

Firstpage

337

Abstract

A parametric recurrent neural network model and an improved dynamic backpropagation method of its learning, are applied for nonlinear plants identification and state estimation. The obtained parameters of the RNN model are used for design of an indirect adaptive control system. The paper suggests three main types of state-space control with RNN state estimation: a proportional; a proportional plus integral and a trajectory-tracking control. The applicability of the proposed neural indirect adaptive control schemes is confirmed by simulation results

Keywords

adaptive control; backpropagation; control system synthesis; neurocontrollers; nonlinear control systems; recurrent neural nets; state estimation; state-space methods; dynamic backpropagation method; identification; indirect adaptive neural control; nonlinear plants; parametric recurrent neural network model; proportional control; proportional plus integral control; state-space control; trajectory-tracking control; Adaptive control; Control system synthesis; Neural networks; Nonlinear dynamical systems; Predictive models; Programmable control; Recurrent neural networks; Stability; State estimation; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.860794

Filename

860794