A neural network method for the nonlinear servomechanism problem

Author

Chu, Yun-Chung ; Huang, Jie

Author_Institution

Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

1

fYear

1998

fDate

21-26 Jun 1998

Firstpage

527

Abstract

The solution of the nonlinear servomechanism problem relies on the solvability of a set of mixed nonlinear partial differential and algebraic equations known as the regulator equations. Due to the nonlinear nature, it is difficult to obtain the exact solution of the regulator equations. This paper proposes to solve the regulator equations based on a class of recurrent neural network, leading to an effective approach to approximately solving the nonlinear servomechanism problem

Keywords

algebra; neurocontrollers; nonlinear control systems; nonlinear differential equations; partial differential equations; recurrent neural nets; servomechanisms; nonlinear algebraic equations; nonlinear partial differential equations; nonlinear servomechanism problem; recurrent neural network; regulator equations; solvability; Automation; Control systems; Differential algebraic equations; Neural networks; Nonlinear control systems; Nonlinear equations; Recurrent neural networks; Regulators; Servomechanisms; State feedback;

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.1998.694724

Filename

694724