Gain-scheduling for systems with repeated scalar nonlinearities

Author

Chu, Yun-Chung ; Glover, Keith

Author_Institution

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

Volume

1

fYear

1997

fDate

10-12 Dec 1997

Firstpage

406

Abstract

The class of nonlinear systems described by a discrete-time state-equation containing a repeated scalar nonlinearity as in recurrent neural networks is considered. Given a plant of this form, sufficient conditions are derived for the synthesis of a controller of the same form so that the induced norm of the closed-loop is under a prescribed level, using positive definite diagonally dominant storage functions. Several of these conditions can be written into linear matrix inequalities

Keywords

control nonlinearities; control system synthesis; discrete time systems; matrix algebra; nonlinear control systems; recurrent neural nets; closed-loop; discrete-time state-equation; gain-scheduling; induced norm; linear matrix inequalities; nonlinear systems; positive definite diagonally dominant storage functions; recurrent neural networks; repeated scalar nonlinearities; sufficient conditions; Automation; Control system synthesis; Linear matrix inequalities; Network synthesis; Neural networks; Nonlinear systems; Recurrent neural networks; Sufficient conditions; Symmetric matrices; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.650657

Filename

650657