  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. 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. This method employs fractional calculus theory to find equations
relating the parameters that are to be estimated, and then estimates the
process parameters after solving the simultaneous equations. The said
simultaneous equations are generated and updated using particle swarm
optimization (PSO) technique, the fitness function being the sum of squared
deviations from the actual set of observations. The data used for the
calculations are intentionally corrupted to simulate real-life conditions.
Results show that the proposed scheme offers a very high degree of accuracy
even for erroneous data.
