Closed-loop Identiication Revisited R E Gl Ertek N Ik a U T O M at Ic Co N T R O L Closed-loop Identiication Revisited ?

In this contribution we study the statistical properties of a number of closed-loop identiication methods and parameterizations. A focus will be on asymptotic variance expressions for these methods. By studying the asymptotic variance for the parameter vector estimates we show that indirect methods fail to give better accuracy than the direct method. Some new results for the bias distribution of the direct method will be presented and we also show how diierent methods correspond to diierent parameterizations and how direct and indirect identiication can be linked together via the noise model. In addition, analysis of a new method for closed-loop identiication shows that it allows tting the model to data with arbitrary frequency weighting under quite general conditions.