Neural network control for non-affine nonlinear systems

Shuzhi Sam Ge, Beibei Ren

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

3 Scopus citations

Abstract

Recently, adaptive neural control has been attracting an increasing attention for nonlinear unknown dynamic systems [1][2]. This paper is dedicated to the discussions on a few techniques in the design of adaptive neural network control for non-affine systems which are known to be difficult to control. The techniques include implicit function theorem based neural control for classes of the non-affine systems in Brunovsky form, implicit function theorem with backstepping design for classes of the non-affine systems in pure-feedback form, and pseudo inverse control. This paper is aimed to provide an overview of the state of art of stable control design for non-affine systems using neural network parametrization, and to list the advantages and disadvantages of neural network control.

Original languageEnglish
Title of host publication2007 European Control Conference, ECC 2007
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4449-4450
Number of pages2
ISBN (Electronic)9783952417386
DOIs
StatePublished - 2007
Event2007 9th European Control Conference, ECC 2007 - Kos, Greece
Duration: Jul 2 2007Jul 5 2007

Publication series

Name2007 European Control Conference, ECC 2007

Conference

Conference2007 9th European Control Conference, ECC 2007
CountryGreece
CityKos
Period07/2/0707/5/07

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    Ge, S. S., & Ren, B. (2007). Neural network control for non-affine nonlinear systems. In 2007 European Control Conference, ECC 2007 (pp. 4449-4450). [7069029] (2007 European Control Conference, ECC 2007). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ecc.2007.7069029