Identification of nonlinear errors-in-variables systems in state-space form: A linear parameter varying approach

In this work, we consider identification of nonlinear errors-in-variables (EIV) system in the state-space form. As the direct identification of nonlinear structured model is difficult, the proposed strategy identifies nonlinear process as a linear parameter varying (LPV) EIV system. The method involves identification of various local linear models along an operating trajectory, and aggregating them using weights computed by means of local model identities. The entire problem is formulated as a maximum likelihood (ML) estimation problem, and is solved using the EM algorithm to obtain the LPV EIV model parameters. A pilot-scale experimental study is employed to demonstrate the effectiveness of the proposed approach.