Prediction error method for identification of LPV models

This paper is concerned with identification of linear parameter varying (LPV) systems in an input–output setting with Box–Jenkins (BJ) model structure. Classical linear time invariant prediction error method (PEM) is extended to the LPV PEM. Under the new LPV framework, identification of two types of input–output LPV models is considered: one is based on parameter interpolation and the other is based on model interpolation. The effectiveness of the proposed solution is validated by comparison with other existing LPV identification approaches through simulation examples and demonstrated by experiment studies.

► Widely used prediction error methods (PEMs) for the identification of linear systems are extended to the identification of linear parameter varying processes.
► By using the Box–Jenkins model structure, colored noise is taken into account instead of the white noise employed as in most of the references in the literature.
► Identification of both model interpolation based input output LPV (MI-IO-LPV) model structure and parameter interpolation based input output LPV (PI-IO-LPV) model structure is considered under the unified PEM framework and improved results are obtained.
► The improvement has been verified by simulations as well as experimental results.