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 :
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