Identification and control of dynamical system by one neural network

Author

Tsuji, Toshio ; Xu, Bing Hong ; Kaneko, Makoto

Author_Institution

Dept. of Ind. & Syst. Eng., Hiroshima Univ., Japan

fYear

1996

fDate

2-6 Dec 1996

Firstpage

701

Lastpage

705

Abstract

The paper proposes a new neural control scheme that can perform identification and control for a dynamical system with linear and nonlinear uncertainties. This scheme uses a single neural network for both the identification and the control. By using the Lyapunov stability technique, stability of the proposed scheme is analyzed and a sufficient condition of the local asymptotic stability is derived. Then, a computer simulation is performed in order to illustrate the effectiveness and the applicability of the proposed scheme

Keywords

Lyapunov methods; asymptotic stability; closed loop systems; feedback; identification; linear systems; neurocontrollers; nonlinear dynamical systems; uncertain systems; Lyapunov stability; asymptotic stability; closed loop systems; dynamical system; feedback; identification; linear systems; neural control; nonlinear systems; uncertain systems; Adaptive control; Asymptotic stability; Control systems; Feedback control; Neural networks; Neurofeedback; Polynomials; Stability analysis; Transfer functions; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

0-7803-3104-4

Type

conf

DOI

10.1109/ICIT.1996.601685

Filename

601685