Parameter identification of a fractional order dynamical system using particle swarm optimization technique
ABSTRACT This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Accurate estimation is particularly important for systems having varying parameters, which is the usual case with physical processes. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme of estimating the parameters for such a fractional order process is proposed in this paper. A population of process models is generated and updated by PSO technique, the fitness function being the sum of squared deviations from the actual set of observations. Results show that the proposed scheme offers a high degree of accuracy.
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ABSTRACT: In this paper, the order distribution concept in the frequency domain identification has been extended to include fractional order systems having poles and zeros simultaneously. The existing nonlinear optimization problem appeared when both poles and zeros, is are changed to a quadratic problem that can be solved using least squares algorithms. To collect the required data, system is excited by a multi sine input signal with appropriately selected frequencies. Then a nonparametric identification in frequency domain is accomplished to calculate the empirical transfer function estimate (ETFE). This estimate is then used to implement the frequency domain identification on all defined members of the model set to estimate the model parameters in noise free and disturbed cases.Signal Processing 01/2010; 90:2243-2252. · 2.24 Impact Factor