Neural network control for non-affine nonlinear systems

Shuzhi Sam Ge; Beibei Ren

doi:10.23919/ecc.2007.7069029

Neural network control for non-affine nonlinear systems

Shuzhi Sam Ge, Beibei Ren

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2007 European Control Conference, ECC 2007
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4449-4450
Number of pages	2
ISBN (Electronic)	9783952417386
DOIs	https://doi.org/10.23919/ecc.2007.7069029
State	Published - 2007
Event	2007 9th European Control Conference, ECC 2007 - Kos, Greece Duration: Jul 2 2007 → Jul 5 2007

Publication series

Name	2007 European Control Conference, ECC 2007

Conference

Conference	2007 9th European Control Conference, ECC 2007
Country	Greece
City	Kos
Period	07/2/07 → 07/5/07

Access to Document

10.23919/ecc.2007.7069029

Link to publication in Scopus

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Cite this

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

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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.",

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AB - 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.

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