Parameter identification of a fractional order dynamical system using particle swarm optimization technique

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.