A direct maximum likelihood optimization approach to identification of LPV time-delay systems

This paper is concerned with parameter estimation for a single-input single-output (SISO) linear parameter varying (LPV) system in an input output setting with output-error (OE) time-delay model structure. Since the practical industrial processes are inherently nonlinear and are often operated over several working points with transition dynamic periods between different working points, the multiple model LPV model is considered in this paper. A global maximization method is firstly used to estimate an autoregressive with exogenous input (ARX) time-delay model for each local process in a noniterative way. Then the Maximum Likelihood (ML) estimator is developed to identify a global LPV OE model based on the local process data and the transition data with the parameters initialized based on the local parameter estimates for the ARX time-delay models. One numerical example and two practical simulation examples are presented to demonstrate the effectiveness of the proposed method. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.