Neural network-based control design: An LMI approach

Suttipan Limanond, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


In this paper we address a neural network-based control design for a discrete-time nonlinear system. Our design approach is to approximate the nonlinear system with a multilayer perception of which the activation functions are of the sigmoid type symmetric to the origin. A linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state-feedback control law for the nonlinear system based on the Certainty Equivalence Principle. The control design equations are shown to be a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal. Further, the stability of the closed-loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed-loop system converges. Finally, a simple example is presented so as to illustrate our control design procedure.

Original languageEnglish (US)
Title of host publicationProceedings of the 1998 American Control Conference, ACC 1998
Number of pages5
StatePublished - Dec 1 1998
Event1998 American Control Conference, ACC 1998 - Philadelphia, PA, United States
Duration: Jun 24 1998Jun 26 1998

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other1998 American Control Conference, ACC 1998
Country/TerritoryUnited States
CityPhiladelphia, PA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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