Implementations of learning control systems using neural networks

Author

Sartori, Michael A. ; Antsaklis, Panos J.

Author_Institution

Dept. of Electr. Eng., Notre Dame Univ., IN, USA

Volume

12

Issue

2

fYear

1992

fDate

4/1/1992 12:00:00 AM

Firstpage

49

Lastpage

57

Abstract

The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.<>

Keywords

learning systems; neural nets; control law scheduling; dynamical system modeling; fault detection; fault identification; information extraction; input/output data points; learning control systems; neural networks; parameter design method; Control system synthesis; Control systems; Design methodology; Dynamic scheduling; Fault diagnosis; Intelligent control; Intelligent networks; Linearity; Neural network hardware; Neural networks;

fLanguage

English

Journal_Title

Control Systems, IEEE

Publisher

ieee

ISSN

1066-033X

Type

jour

DOI

10.1109/37.126853

Filename

126853