Accessibility navigation

Stable linearization using multilayer neural networks

Delgado, A., Kambhampati, C. and Warwick, K. (1996) Stable linearization using multilayer neural networks. In: UKACC International Conference on Control, 2-5 Sep 1996, Exeter, UK, pp. 194-198.

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Official URL:


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.

Item Type:Conference or Workshop Item (Paper)
ID Code:21651
Uncontrolled Keywords: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

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation