Application of the PLS–PH Method for Identifying Polynomial NARX Models

Abstract

Nowadays, MPC controllers are widely applied in the industry, especially in chemical and petrochemical processes. In order to obtain good models for these controllers, a family of identification methods specific for them has been developed, namely the MPC relevant identification (MRI) methods. One of the algorithms of this family is the PLS–PH (partial least squares–prediction horizon), described in Lauri et al. (Chemometrics and Intelligent Laboratory Systems 100:118–126, 2010). The version of the MPC controllers based on nonlinear models, known as NMPC, has a much more restricted application, because they consist of more difficult optimization problems and present greater complexity for obtaining good nonlinear models for the process. In order to circumvent this difficulty, this article presents an extension of the family of MRI identification methods for obtaining models for NMPC controllers. This extension is accomplished by means of the PLS–PH algorithm for identifying models with nonlinear autoregressive with exogenous inputs (NARX) polynomial structure. An application of this method is also presented for identifying a nonlinear model based on data collected from an electric oven.