Adaptive output feedback control for general nonlinear systems using multilayer neural networks

Author

Zhang, T. ; Ge, S.S. ; Hang, C.C.

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

1

fYear

1998

fDate

21-26 Jun 1998

Firstpage

520

Abstract

In this paper, the adaptive output feedback control problem is investigated using multilayer neural networks (MNNs) for a class of general nonlinear systems. The adaptive output feedback controller is developed based on a high-gain observer which is used to estimate the time derivatives of the system output. The Lyapunov stability of the resulting closed-loop system is guaranteed and the tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed controller is illustrated through an example of composition control for a continuously stirred tank reactor (CSTR) system

Keywords

Lyapunov methods; adaptive control; closed loop systems; feedback; multilayer perceptrons; neurocontrollers; nonlinear control systems; observers; stability; CSTR system; Lyapunov stability; MNN; adaptive output feedback control; closed-loop system; composition control; continuously stirred tank reactor system; general nonlinear systems; high-gain observer; multilayer neural networks; time derivative estimation; tracking error convergence; Adaptive control; Adaptive systems; Control systems; Lyapunov method; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Output feedback; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1998. Proceedings of the 1998

Conference_Location

Philadelphia, PA

ISSN

0743-1619

Print_ISBN

0-7803-4530-4

Type

conf

DOI

10.1109/ACC.1998.694722

Filename

694722