DocumentCode
3497501
Title
Inverting recurrent neural networks for internal model control of nonlinear systems
Author
Kambhampati, C. ; Craddock, R. ; Tham, M. ; Warwick, K.
Author_Institution
Dept. of Cybern., Reading Univ., UK
Volume
2
fYear
1998
fDate
21-26 Jun 1998
Firstpage
975
Abstract
In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated
Keywords
closed loop systems; model reference adaptive control systems; neurocontrollers; nonlinear control systems; recurrent neural nets; stability; closed-loop controller; internal model control; internal model control system; nonlinear systems; recurrent neural network invertibility; recurrent neural network relative order determination; Control system synthesis; Control systems; Cybernetics; Filters; Inverse problems; Neural networks; Nonlinear control systems; Nonlinear systems; Recurrent neural networks; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1998. Proceedings of the 1998
Conference_Location
Philadelphia, PA
ISSN
0743-1619
Print_ISBN
0-7803-4530-4
Type
conf
DOI
10.1109/ACC.1998.703554
Filename
703554
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