Identification and control experiments using neural designs

Author

Mistry, Sanjay I. ; Nair, Satish S.

Author_Institution

Comput. Controlled Syst. Lab., Missouri Univ., Columbia, MO, USA

Volume

14

Issue

3

fYear

1994

fDate

6/1/1994 12:00:00 AM

Firstpage

48

Lastpage

57

Abstract

Neural designs are reported for system identification and control using static and dynamic gradient update schemes. Real-time implementation of the designs using a hardware example case system illustrates the inherent capability of neural networks to handle nonlinearities, learn, and perform control effectively for a real world system, based on minimal system information. The advantages of dynamic schemes over static ones are highlighted and a neural control design with feedforward and feedback components that facilitates incorporation of available knowledge about a system is described.<>

Keywords

control nonlinearities; control system synthesis; feedback; feedforward neural nets; identification; control nonlinearities; dynamic gradient update; feedback; feedforward; feedforward neural networks; neural control design; nonlinear systems; real time implementation; static gradient update; system identification; Control design; Control nonlinearities; Control systems; Neural network hardware; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear dynamical systems; Real time systems; System identification;

fLanguage

English

Journal_Title

Control Systems, IEEE

Publisher

ieee

ISSN

1066-033X

Type

jour

DOI

10.1109/37.291457

Filename

291457