  System identification refers to estimation of process parameters and is a
necessity in control theory. Physical systems usually have varying parameters.
For such processes, accurate identification is particularly important. Online
identification schemes are also needed for designing adaptive controllers. Real
processes are usually of fractional order as opposed to the ideal integral
order models. In this paper, we propose a simple and elegant scheme of
estimating the parameters for such a fractional order process. A population of
process models is generated and updated by particle swarm optimization (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 even when the observations are corrupted to a significant
degree. Additional schemes to improve the accuracy still further are also
proposed and analyzed.
