Neural network-based control design: An LMI approach

Suttipan Limanond, Jennie Si

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

1 Scopus citations

Abstract

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
Pages970-974
Number of pages5
DOIs
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
Volume2
ISSN (Print)0743-1619

Other

Other1998 American Control Conference, ACC 1998
CountryUnited States
CityPhiladelphia, PA
Period6/24/986/26/98

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Limanond, S., & Si, J. (1998). Neural network-based control design: An LMI approach. In Proceedings of the 1998 American Control Conference, ACC 1998 (pp. 970-974). [703553] (Proceedings of the American Control Conference; Vol. 2). https://doi.org/10.1109/ACC.1998.703553