B-spline backstepping control with derivative matrix estimation and its applications

Yih Guang Leu; Chun Yao Chen

doi:10.1016/j.neucom.2010.08.015

B-spline backstepping control with derivative matrix estimation and its applications

Yih Guang Leu^*, Chun Yao Chen

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

A B-spline backstepping controller is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates the backstepping design technique with a B-spline neural network which is utilized to estimate the system dynamics. The B-spline neural network has the advantage of locally controlling its output behavior compared with other neural networks; therefore, it is very suitable to online estimate the system dynamics by tuning its interior parameters, including control points and knot points. Based on the mean-value theorem, the derivative of B-spline basis functions in relation to parameters can be estimated to online adjust these parameters. In addition, the validity of the proposed scheme is verified through an experiment on a servo motor system which is controlled by the output voltage of the Buck DC-DC converter.

Original language	English
Pages (from-to)	499-508
Number of pages	10
Journal	Neurocomputing
Volume	74
Issue number	4
DOIs	https://doi.org/10.1016/j.neucom.2010.08.015
Publication status	Published - 2011 Jan

Keywords

B-spline functions
Backstepping design
Neural networks
Nonlinear control

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2010.08.015

Cite this

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title = "B-spline backstepping control with derivative matrix estimation and its applications",

abstract = "A B-spline backstepping controller is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates the backstepping design technique with a B-spline neural network which is utilized to estimate the system dynamics. The B-spline neural network has the advantage of locally controlling its output behavior compared with other neural networks; therefore, it is very suitable to online estimate the system dynamics by tuning its interior parameters, including control points and knot points. Based on the mean-value theorem, the derivative of B-spline basis functions in relation to parameters can be estimated to online adjust these parameters. In addition, the validity of the proposed scheme is verified through an experiment on a servo motor system which is controlled by the output voltage of the Buck DC-DC converter.",

keywords = "B-spline functions, Backstepping design, Neural networks, Nonlinear control",

author = "Leu, {Yih Guang} and Chen, {Chun Yao}",

note = "Funding Information: This work was supported by the National Science Council of Taiwan , ROC, under Grant no. NSC 96-2221-E-003-012-MY3 . ",

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T1 - B-spline backstepping control with derivative matrix estimation and its applications

AU - Leu, Yih Guang

AU - Chen, Chun Yao

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PY - 2011/1

Y1 - 2011/1

N2 - A B-spline backstepping controller is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates the backstepping design technique with a B-spline neural network which is utilized to estimate the system dynamics. The B-spline neural network has the advantage of locally controlling its output behavior compared with other neural networks; therefore, it is very suitable to online estimate the system dynamics by tuning its interior parameters, including control points and knot points. Based on the mean-value theorem, the derivative of B-spline basis functions in relation to parameters can be estimated to online adjust these parameters. In addition, the validity of the proposed scheme is verified through an experiment on a servo motor system which is controlled by the output voltage of the Buck DC-DC converter.

AB - A B-spline backstepping controller is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates the backstepping design technique with a B-spline neural network which is utilized to estimate the system dynamics. The B-spline neural network has the advantage of locally controlling its output behavior compared with other neural networks; therefore, it is very suitable to online estimate the system dynamics by tuning its interior parameters, including control points and knot points. Based on the mean-value theorem, the derivative of B-spline basis functions in relation to parameters can be estimated to online adjust these parameters. In addition, the validity of the proposed scheme is verified through an experiment on a servo motor system which is controlled by the output voltage of the Buck DC-DC converter.

KW - B-spline functions

KW - Backstepping design

KW - Neural networks

KW - Nonlinear control

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B-spline backstepping control with derivative matrix estimation and its applications

Abstract

Keywords

ASJC Scopus subject areas

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