DocumentCode
2150560
Title
Stable linearization using multilayer neural networks
Author
Delgado, A. ; Kambhampati, C. ; Warwick, K.
Author_Institution
Nat. Univ. of Colombia, Colombia
Volume
1
fYear
1996
fDate
2-5 Sept. 1996
Firstpage
194
Abstract
The main limitation of linearization theory that prevents its application in practical problems is the need for an exact knowledge of the plant. This requirement is eliminated and it is shown that a multilayer network can synthesise the state feedback coefficients that linearize a nonlinear control affine plant. The stability of the linearizing closed loop can be guaranteed if the autonomous plant is asymptotically stable and the state feedback is bounded.
Keywords
asymptotic stability; closed loop systems; feedforward neural nets; linearisation techniques; multilayer perceptrons; neurocontrollers; nonlinear control systems; state feedback; asymptotic stability; autonomous plant; exact knowledge; linearization theory; linearizing closed loop; multilayer network; multilayer neural networks; nonlinear control affine plant; practical problems; stable linearization; state feedback; state feedback coefficients;
fLanguage
English
Publisher
iet
Conference_Titel
Control '96, UKACC International Conference on (Conf. Publ. No. 427)
ISSN
0537-9989
Print_ISBN
0-85296-668-7
Type
conf
DOI
10.1049/cp:19960551
Filename
651378
Link To Document