Skip to main content

Research Repository

Advanced Search

Stable linearization using multilayer neural networks

Delgado, A.; Kambhampati, Chandrasekhar; Warwick, K.


A. Delgado

K. Warwick


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.


Delgado, A., Kambhampati, C., & Warwick, K. (1996). Stable linearization using multilayer neural networks. .

Conference Name UKACC International Conference on Control. Control '96
Conference Location Exeter, UK
Start Date Sep 2, 1996
End Date Sep 5, 1996
Acceptance Date Dec 31, 1996
Online Publication Date Aug 6, 2002
Publication Date 1996
Journal IEE Conference Publication
Print ISSN 0537-9989
Publisher Institution of Engineering and Technology (IET)
Peer Reviewed Peer Reviewed
Volume 427
Issue 1
Pages 194 - 198
ISBN 0852966660
Keywords Asymptotic stability; Multilayer perceptrons; Feedforward neural nets; Neurocontrollers; State feedback; Linearisation techniques; Closed loop systems; Nonlinear control systems
Public URL