Neural network-based control: Multiobjective design

Author

Liu, Fei

Author_Institution

Sch. of Commun. & Control Eng., Southern Yangtze Univ., Jiangsu, China

Volume

5

fYear

2002

fDate

6-9 Oct. 2002

Abstract

By means of linear difference inclusion (LDI) state-space representation, a general design methodology for neural network-based control systems is extended to cover various performance indexes with stability qualification. Considering the given approximation error bound between a nonlinear dynamical system and a multi-layer neural network, the proposed methodology is formulated in a set of linear matrix inequalities (LMIs). By benefiting from the common backpropagation (BP) algorithm for NN´s training and numerical solver for LMI, the design approach shows its availability.

Keywords

linear matrix inequalities; neural nets; neurocontrollers; nonlinear control systems; state-space methods; approximation error bound; backpropagation algorithm; linear difference inclusion state-space representation; linear matrix inequalities; multiobjective design; neural network-based control; nonlinear dynamical system; performance indexes; stability qualification; Approximation error; Control systems; Design methodology; Linear matrix inequalities; Multi-layer neural network; Neural networks; Nonlinear dynamical systems; Performance analysis; Qualifications; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2002 IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-7437-1

Type

conf

DOI

10.1109/ICSMC.2002.1176370

Filename

1176370