Global exponential stability of recurrent neural networks for synthesizing linear feedback control systems via pole assignment

Author

Zhang, Yunong ; Wang, Jun

Author_Institution

Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China

Volume

13

Issue

3

fYear

2002

fDate

5/1/2002 12:00:00 AM

Firstpage

633

Lastpage

644

Abstract

Global exponential stability is the most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements

Keywords

asymptotic stability; control system synthesis; feedback; linear systems; multivariable control systems; neurocontrollers; pole assignment; recurrent neural nets; convergence rates; design parameter selection; feedback gains; global exponential stability; linear feedback control systems; linear time-invariant multivariable systems; online computation; pole assignment; recurrent neural networks; simulation; Computational modeling; Computer networks; Control system synthesis; Convergence; Gain; MIMO; Network synthesis; Neurofeedback; Recurrent neural networks; Stability analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2002.1000129

Filename

1000129