Abstract
This paper is concerned with the problem of a fuzzy filter of nonlinear system with missing measurements. The nonlinear system is represented by a Takagi-Sugeno(TS) fuzzy model. The system measurements may be unavailable at any sample time and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, the error state of the filtering process is mean square bounded. A basis-dependent Lyapunov function approach is developed to design the fuzzy filter, and it is developed the upper bound of a fuzzy filter gain of the estimation error subject to some LMI constraints. In this situation, the estimation error due to persistent bounded disturbances. Finally, an illustrative numerical example is provided to show the effectiveness of the proposed approach.
| Original language | English |
|---|---|
| Title of host publication | ICCAS 2010 - International Conference on Control, Automation and Systems |
| Pages | 663-667 |
| Number of pages | 5 |
| Publication status | Published - 2010 Dec 1 |
| Event | International Conference on Control, Automation and Systems, ICCAS 2010 - Gyeonggi-do, Korea, Republic of Duration: 2010 Oct 27 → 2010 Oct 30 |
Other
| Other | International Conference on Control, Automation and Systems, ICCAS 2010 |
|---|---|
| Country | Korea, Republic of |
| City | Gyeonggi-do |
| Period | 10/10/27 → 10/10/30 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Control and Systems Engineering
Cite this
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A fuzzy filter with missing measurement for observer-based T-S fuzzy models. / Noh, Sun Young; Park, Jin Bae; Joo, Young Hoon.
ICCAS 2010 - International Conference on Control, Automation and Systems. 2010. p. 663-667 5670231.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A fuzzy filter with missing measurement for observer-based T-S fuzzy models
AU - Noh, Sun Young
AU - Park, Jin Bae
AU - Joo, Young Hoon
PY - 2010/12/1
Y1 - 2010/12/1
N2 - This paper is concerned with the problem of a fuzzy filter of nonlinear system with missing measurements. The nonlinear system is represented by a Takagi-Sugeno(TS) fuzzy model. The system measurements may be unavailable at any sample time and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, the error state of the filtering process is mean square bounded. A basis-dependent Lyapunov function approach is developed to design the fuzzy filter, and it is developed the upper bound of a fuzzy filter gain of the estimation error subject to some LMI constraints. In this situation, the estimation error due to persistent bounded disturbances. Finally, an illustrative numerical example is provided to show the effectiveness of the proposed approach.
AB - This paper is concerned with the problem of a fuzzy filter of nonlinear system with missing measurements. The nonlinear system is represented by a Takagi-Sugeno(TS) fuzzy model. The system measurements may be unavailable at any sample time and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, the error state of the filtering process is mean square bounded. A basis-dependent Lyapunov function approach is developed to design the fuzzy filter, and it is developed the upper bound of a fuzzy filter gain of the estimation error subject to some LMI constraints. In this situation, the estimation error due to persistent bounded disturbances. Finally, an illustrative numerical example is provided to show the effectiveness of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=78751559469&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78751559469&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:78751559469
SN - 9781424474530
SP - 663
EP - 667
BT - ICCAS 2010 - International Conference on Control, Automation and Systems
ER -