• 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