Stochastic distribution control of singular systems

output PDF shaping

H. Yue, A.J.A. Leprand, H. Wang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper presents a new algorithm designed to control the shape of the output probability density function (PDF) of singular systems subjected to non-Gaussian input. The aim is to select a control input uk such that the output PDF is made as close as possible to a given PDF. Based on the B-spline neural network approximation of the output PDF, the control algorithm is formulated by extending the developed PDF control strategies of non-singular systems to singular systems. It has been shown that under certain conditions the stability of the closed-loop system can be guaranteed. Simulation examples are given to show the effectiveness of the proposed control algorithm
Original languageEnglish
Pages (from-to)151-160
Number of pages10
JournalActa Automatica Sinica
Volume31
Issue number1
Publication statusPublished - Jan 2005

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Probability density function
Closed loop systems
Splines
Neural networks

Keywords

  • singular systems
  • dynamic stochastic systems
  • probability density function (PDF)
  • B-splines neural networks

Cite this

Yue, H. ; Leprand, A.J.A. ; Wang, H. / Stochastic distribution control of singular systems : output PDF shaping. In: Acta Automatica Sinica. 2005 ; Vol. 31, No. 1. pp. 151-160.
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Stochastic distribution control of singular systems : output PDF shaping. / Yue, H.; Leprand, A.J.A.; Wang, H.

In: Acta Automatica Sinica, Vol. 31, No. 1, 01.2005, p. 151-160.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Stochastic distribution control of singular systems

T2 - output PDF shaping

AU - Yue, H.

AU - Leprand, A.J.A.

AU - Wang, H.

PY - 2005/1

Y1 - 2005/1

N2 - This paper presents a new algorithm designed to control the shape of the output probability density function (PDF) of singular systems subjected to non-Gaussian input. The aim is to select a control input uk such that the output PDF is made as close as possible to a given PDF. Based on the B-spline neural network approximation of the output PDF, the control algorithm is formulated by extending the developed PDF control strategies of non-singular systems to singular systems. It has been shown that under certain conditions the stability of the closed-loop system can be guaranteed. Simulation examples are given to show the effectiveness of the proposed control algorithm

AB - This paper presents a new algorithm designed to control the shape of the output probability density function (PDF) of singular systems subjected to non-Gaussian input. The aim is to select a control input uk such that the output PDF is made as close as possible to a given PDF. Based on the B-spline neural network approximation of the output PDF, the control algorithm is formulated by extending the developed PDF control strategies of non-singular systems to singular systems. It has been shown that under certain conditions the stability of the closed-loop system can be guaranteed. Simulation examples are given to show the effectiveness of the proposed control algorithm

KW - singular systems

KW - dynamic stochastic systems

KW - probability density function (PDF)

KW - B-splines neural networks

M3 - Article

VL - 31

SP - 151

EP - 160

JO - Acta Automatica Sinica

JF - Acta Automatica Sinica

SN - 1874-1029

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ER -