Parallel-distributed Kalman filter for discrete-time nonlinear systems

Author

Chiang, Tung-sheng ; Lin, Jonqlan

Author_Institution

Dept. of Electr. Eng., Ching-Yun Inst. of Technol., Chung-li, Taiwan

Volume

2

fYear

2003

Firstpage

1074

Abstract

In this paper, a parallel-distributed Kalman filter (PDKF) for discrete-time nonlinear systems to estimate the states is proposed. Based on T-S fuzzy model, the nonlinear system can be exactly represented in form of T-S fuzzy model. Then, PDKFs are designed for each subsystem. The advantage of PDKF is greatly reduces the computational complexity of the global filter. We use a nonlinear system as an example to verify the proposed algorithm. Numerical simulation can verify the theoretical derivations.

Keywords

Henon mapping; Kalman filters; computational complexity; discrete time systems; filtering theory; fuzzy logic; matrix algebra; nonlinear estimation; nonlinear systems; state estimation; Numerical simulation; T-S fuzzy model; computational complexity; data analysis; discrete time nonlinear systems; distributed Kalman filter estimation; fault diagnosis; fuzzy logic; fuzzy set theory; global filter; parallel distributed kalman filter; process control; proposed algorithm; theoretical derivations; Fuzzy control; Fuzzy sets; Fuzzy systems; Linear systems; Noise measurement; Nonlinear equations; Nonlinear systems; Power system modeling; State estimation; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on

Print_ISBN

0-7803-7810-5

Type

conf

DOI

10.1109/FUZZ.2003.1206581

Filename

1206581