Reformulation of parameter identification with unknown-but-bounded errors

In this paper a new formulation of the problem of identification of discrete-time linear models in the case of Unknown-But-Bounded errors is proposed. The bounds of the error at each sampling time are specified over a measurement noise rather than over an equation error, which is mainly motivated by experimental considerations. The method is particularly suitable for ARMAX models, as it accounts for the presence of uncertainties in the autoregressive terms.