DocumentCode :
630649
Title :
Identification of fractional order systems using modulating functions method
Author :
Da-Yan Liu ; Laleg-Kirati, Taous-Meriem ; Gibaru, Olivier ; Perruquetti, W.
Author_Institution :
Comput., Electr. & Math. Sci. & Eng. Div., King Abdullah Univ. of Sci. & Technol., Thuwal, Saudi Arabia
fYear :
2013
fDate :
17-19 June 2013
Firstpage :
1679
Lastpage :
1684
Abstract :
The modulating functions method has been used for the identification of linear and nonlinear systems. In this paper, we generalize this method to the on-line identification of fractional order systems based on the Riemann-Liouville fractional derivatives. First, a new fractional integration by parts formula involving the fractional derivative of a modulating function is given. Then, we apply this formula to a fractional order system, for which the fractional derivatives of the input and the output can be transferred into the ones of the modulating functions. By choosing a set of modulating functions, a linear system of algebraic equations is obtained. Hence, the unknown parameters of a fractional order system can be estimated by solving a linear system. Using this method, we do not need any initial values which are usually unknown and not equal to zero. Also we do not need to estimate the fractional derivatives of noisy output. Moreover, it is shown that the proposed estimators are robust against high frequency sinusoidal noises and the ones due to a class of stochastic processes. Finally, the efficiency and the stability of the proposed method is confirmed by some numerical simulations.
Keywords :
differential equations; integration; numerical analysis; parameter estimation; stochastic processes; Riemann-Liouville fractional derivatives; fractional integration-by-parts formula; high-frequency sinusoidal noise robustness; linear algebraic equation system; modulating function method; numerical simulations; online fractional order system identification; stochastic processes; unknown parameter estimation; Differential equations; Estimation; Laplace equations; Linear systems; Mathematical model; Noise; Noise measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2013
Conference_Location :
Washington, DC
ISSN :
0743-1619
Print_ISBN :
978-1-4799-0177-7
Type :
conf
DOI :
10.1109/ACC.2013.6580077
Filename :
6580077
Link To Document :
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