DocumentCode :
2951198
Title :
A fuzzy-smooth variable structure filtering strategy: For state and parameter estimation
Author :
Gadsden, S. Andrew ; Al-Shabi, Mohammad A. ; Habibi, Saeid R.
Author_Institution :
Dept. of Mech. Eng., McMaster Univ. Hamilton, Hamilton, ON, Canada
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
1
Lastpage :
6
Abstract :
This article introduces a new filtering strategy based on combining elements of fuzzy logic and the smooth variable structure filter (SVSF). A revised formulation of the SVSF is presented in an effort to combine it with fuzzy logic, and is referred to as the RSVSF. Computer simulations are used to compare the new strategy, referred to as the Fuzzy-SVSF, with other popular Kalman-based estimation methods. Preliminary results indicate that combining fuzzy logic with the SVSF yields an improved estimation result and improved stability to system and modeling changes and uncertainties.
Keywords :
Kalman filters; fuzzy logic; fuzzy set theory; state estimation; Kalman-based estimation methods; SVSF; computer simulations; fuzzy logic; fuzzy-smooth variable structure filtering strategy; parameter estimation; state estimation; Covariance matrices; Equations; Estimation; Fuzzy logic; Mathematical model; Noise; Uncertainty; Kalman filter; estimation; fuzzy logic; smooth variable structure filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on
Conference_Location :
Amman
Print_ISBN :
978-1-4799-2305-2
Type :
conf
DOI :
10.1109/AEECT.2013.6716481
Filename :
6716481
Link To Document :
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