Parameter Estimation of Fractional-Order Systems via Evolutionary Algorithms and the Extended Fractional Kalman Filter

Guel-Cortez, Adrian JosueCoventry University, Centre for fluid and complex systems, Coventry, United Kingdom (författare)

Part of ISBN 9798350321685QC 20230821
As fractional-order models increasingly appear as an option to describe complex systems, they generate a demand for parameter estimation methods in the time and frequency domain. The extended Kalman filter (EKF) is a promising technique in the time domain, but it is sensitive to the initial conditions of the state and error covariance matrices. In the case of integer-order systems, evolutionary algorithms (EAs) can tackle EKF's sensitiveness issues. The algorithm usually uses EAs to optimise the initial conditions for the EK, leading to a better estimate of the system parameters and states. Here, we extend this methodology to fractional-order models to estimate the model's fractional order and parameters. Finally, we demonstrate the effectiveness of this methodology on a simple mechanical model.

Kim, Eun JinCoventry University, Centre for fluid and complex systems, Coventry, United Kingdom (författare)
Chamorro Vera, Harold ReneKTH,Elkraftteknik(Swepub:kth)u12rc2ga (författare)
Coventry University, Centre for fluid and complex systems, Coventry, United KingdomElkraftteknik (creator_code:org_t)

Ingår i:2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023: Institute of Electrical and Electronics Engineers (IEEE)

Av författaren/redakt...: Guel-Cortez, Adr ...; Kim, Eun Jin; Chamorro Vera, H ...

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