Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Fractional system identification for lead acid battery state of charge estimation
Signal Processing - Fractional calculus applications in signals and systems
Brief paper: Synthesis of fractional Laguerre basis for system approximation
Automatica (Journal of IFAC)
Identification of linear dynamic systems operating in a networked environment
Automatica (Journal of IFAC)
LMI stability conditions for fractional order systems
Computers & Mathematics with Applications
Identification of Continuous-time Models from Sampled Data
Identification of Continuous-time Models from Sampled Data
Fractional modelling and identification of thermal systems
Signal Processing
Studies on fractional order differentiators and integrators: A survey
Signal Processing
Estimation of thermal parameters using fractional modelling
Signal Processing
Brief paper: Analytical computation of the H2-norm of fractional commensurate transfer functions
Automatica (Journal of IFAC)
Brief paper: Stability and resonance conditions of elementary fractional transfer functions
Automatica (Journal of IFAC)
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
Correspondence item: An instrumental product moment test for model order estimation
Automatica (Journal of IFAC)
Computers & Mathematics with Applications
| Hi-index | 22.14 |
This paper deals with continuous-time system identification using fractional differentiation models. An adapted version of the simplified refined instrumental variable method is first proposed to estimate the parameters of the fractional model when all the differentiation orders are assumed known. Then, an optimization approach based on the use of the developed instrumental variable estimator is presented. Two variants of the algorithm are proposed. Either, all differentiation orders are set as integral multiples of a commensurate order which is estimated, or all differentiation orders are estimated. The former variant allows to reduce the number of parameters and can be used as a good initial hit for the latter variant. The performances of the proposed approaches are evaluated by Monte Carlo simulation analysis. Finally, the proposed identification algorithms are used to identify thermal diffusion in an experimental setup.