Recursive system identification for coefficient estimation of continuous-time fractional order systems

Abstract

Fractional-order calculus has already proven to be effective in order to model diffusive phenomena, such as heat transfer or anomalous mass transfer. This has led to developments of fractional-order transfer function models and, as a consequence, system identification algorithms have been developed to identify the parameters for this type of structures. However, it may be necessary to perform real-time system identification in which the parameters evolve over time while more and more data is acquired. Extensions to recursive identification algorithms are presented for fractional-order transfer functions. Two methods are proposed for recursive estimation of the coefficients of continuous-time fractional order systems: the recursive least squares with state variable filters and the prediction-error method. These two methods are compared with different signal to noise ratio levels through Monte Carlo simulations.