Dynamic grey clustering adaptive H∞ filtering algorithm

Shao Zhong Dai; Bo Wang; Yong Sheng Shi; Ming Jie Dong

Dynamic grey clustering adaptive H_∞ filtering algorithm

Shao Zhong Dai^*, Bo Wang, Yong Sheng Shi, Ming Jie Dong

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

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

Abstract

To solve the problem that parameters of H_∞ filter increase with time, a new adaptive H_∞ filter algorithm is proposed. Based on real-time estimation of noise matrix and grey clustering of state variable, filter or gain matrix is modulated so as to get an estimation of the state vector. Simulation showed that, using the new algorithm, compared with the H_∞ or Kalman filter, expected precision could be achieved and parameters could be made stable in 10 s. The improved algorithm can avoid spreading of computation and has excellent robustness.

Original language	English
Pages (from-to)	995-998
Number of pages	4
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	26
Issue number	11
Publication status	Published - Nov 2006

Keywords

Grey clustering
H filter
Robustness

Cite this

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title = "Dynamic grey clustering adaptive H∞ filtering algorithm",

abstract = "To solve the problem that parameters of H∞ filter increase with time, a new adaptive H∞ filter algorithm is proposed. Based on real-time estimation of noise matrix and grey clustering of state variable, filter or gain matrix is modulated so as to get an estimation of the state vector. Simulation showed that, using the new algorithm, compared with the H∞ or Kalman filter, expected precision could be achieved and parameters could be made stable in 10 s. The improved algorithm can avoid spreading of computation and has excellent robustness.",

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T1 - Dynamic grey clustering adaptive H∞ filtering algorithm

AU - Dai, Shao Zhong

AU - Wang, Bo

AU - Shi, Yong Sheng

AU - Dong, Ming Jie

PY - 2006/11

Y1 - 2006/11

N2 - To solve the problem that parameters of H∞ filter increase with time, a new adaptive H∞ filter algorithm is proposed. Based on real-time estimation of noise matrix and grey clustering of state variable, filter or gain matrix is modulated so as to get an estimation of the state vector. Simulation showed that, using the new algorithm, compared with the H∞ or Kalman filter, expected precision could be achieved and parameters could be made stable in 10 s. The improved algorithm can avoid spreading of computation and has excellent robustness.

AB - To solve the problem that parameters of H∞ filter increase with time, a new adaptive H∞ filter algorithm is proposed. Based on real-time estimation of noise matrix and grey clustering of state variable, filter or gain matrix is modulated so as to get an estimation of the state vector. Simulation showed that, using the new algorithm, compared with the H∞ or Kalman filter, expected precision could be achieved and parameters could be made stable in 10 s. The improved algorithm can avoid spreading of computation and has excellent robustness.

KW - Grey clustering

KW - H filter

KW - Robustness

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M3 - Article

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JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

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ER -

Dynamic grey clustering adaptive H_∞ filtering algorithm

Abstract

Keywords

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