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Research Papers

Nonfragile H Filtering of Continuous Markov Jump Linear Systems With General Transition Probabilities

[+] Author and Article Information
Mouquan Shen

College of Automation and Electrical Engineering,
Nanjing University of Technology,
Nanjing 211816, China
e-mail: mouquanshen@gmail.com

Guang-Hong Yang

College of Information Science and Engineering,
State Key Laboratory of Synthetical Automation for Process Industries,
Northeastern University,
Shenyang 110819, China
e-mail: yangguanghong@ise.neu.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 8, 2012; final manuscript received January 9, 2013; published online February 21, 2013. Assoc. Editor: Yang Shi.

J. Dyn. Sys., Meas., Control 135(3), 031005 (Feb 21, 2013) (8 pages) Paper No: DS-12-1254; doi: 10.1115/1.4023403 History: Received August 08, 2012; Revised January 09, 2013

This paper concerns the mode dependent H filter design for continuous Markov jump linear systems. The filter gain to be designed is assumed to have additive variations and the transition probabilities are allowed to be known, uncertain with known bounds and unknown. Attention is focused on the design of a mode dependent nonfragile full order filter, which guarantees the filtering error system to be stochastically stable and has a prescribed H disturbance attenuation performance. Sufficient conditions for the desired filter design are given in the framework of linear matrix inequality. If the filter gain variations become zero and the transition probabilities are completely known, the proposed method is reduced to the standard H filtering results. A numerical examples is given to show the effectiveness of the proposed method.

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References

Figures

Grahic Jump Location
Fig. 1

One possible system mode

Grahic Jump Location
Fig. 2

Estimation error e(t) curves for partly known with nonfragile (solid) and without nonfragile (dashed)

Grahic Jump Location
Fig. 3

Estimation error e(t) curves for completely known with nonfragile (solid) and without nonfragile (dashed)

Grahic Jump Location
Fig. 4

Estimation error e(t) curves for completely known (solid) and partly known with nonfragile (dashed)

Grahic Jump Location
Fig. 5

Estimation error e(t) curves for completely known (solid) and partly known without nonfragile (dashed)

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