Robust adaptive control via neural linearization and four types of compensation

Author

Yu, Wen ; Li, XiaoOu

Author_Institution

Dept. de Control Automatico, CINVESTAV-IPN, Mexico City

fYear

2008

fDate

1-8 June 2008

Firstpage

1807

Lastpage

1813

Abstract

In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.

Keywords

adaptive control; closed loop systems; feedback; linearisation techniques; neurocontrollers; nonlinear control systems; stability; closed-loop system stability; dead-zone technique; dynamic neural networks; neural adaptive control; neural identification stability; neural identifier-based feedback linearization controller; neural linearization; projection technique; robust adaptive control; unknown nonlinear systems; Adaptive control; Control systems; Linear feedback control systems; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Robust control; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Conference_Location

Hong Kong

ISSN

1098-7576

Print_ISBN

978-1-4244-1820-6

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2008.4634043

Filename

4634043