| Stochastic Distribution Control of Singular Systems: Output PDF Shaping |
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| 作者姓名： | H.YUE A.J.A.LEPRAND H.WANG |
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| 作者单位： | 1.Institute of Automation, Chinese Academy of Sciences, Beijing 100080 P.R.China |
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| 基金项目： | Supported by the Research Fund of Chinese Academy of Sciences (2004-1-4) |
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| 摘 要： | 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.
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| 关 键 词： | Singular systems dynamic stochastic systems probability density function (PDF) B-splines neural networks |
| 收稿时间： | 2004-5-2 |
| 修稿时间： | 2004-9-22 |
Stochastic Distribution Control of Singular Systems: Output PDF Shaping |
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| H.YUE,A.J.A.LEPRAND,H.WANG.Stochastic Distribution Control of Singular Systems: Output PDF Shaping[J].Acta Automatica Sinica,2005,31(1):151-160. |
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| Authors: | HYUE AJALEPRAND HWang |
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| Affiliation: | 1.Institute of Automation, Chinese Academy of Sciences, Beijing 100080 P.R.China |
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| 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. |
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| Keywords: | Singular systems dynamic stochastic systems probability density function (PDF) B-splines neural networks |
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