Refined instrumental variable methods for identification of LPV Box–Jenkins models

The identification of linear parameter-varying systems in an input–output setting is investigated, focusing on the case when the noise part of the data generating system is an additive colored noise. In the Box–Jenkins and output-error cases, it is shown that the currently available linear regression and instrumental variable methods from the literature are far from being optimal in terms of bias and variance of the estimates. To overcome the underlying problems, a refined instrumental variable method is introduced. The proposed approach is compared to the existing methods via a representative simulation example.