No abstract

This paper presents an up to date advances in time-domain system identification using fractional models. Both equation-error- and output-error-based models are detailed. In the former models, prior knowledge is generally used to fix differentiation orders; model coefficients are estimated using least squares. The latter models allow simultaneous estimation of model coefficients and differentiation orders using nonlinear programing. As an example, a thermal system is identified using a fractional model and is compared to a rational one.

this paper deals with continuous-time system identification using fractional differentiation models in a noisy output context. The simplified refined instrumental variable for continuous-time systems (srivc) is extended to fractional models. Monte Carlo simulation analysis are used to demonstrate the performance of the proposed optimal instrumental variable scheme.