Non-linear model approximation and reduction by new input-state Hammerstein block structure

Abstract

Abstract In this paper, the focus will be on approximating nonlinear large scale mathematical model of process systems using full order block-structured model. Further, the objective is to achieve a reduced order model for the nonlinear large model with reduced computational complexity, while at the same time being the good approximation of nonlinear model. The modeling approach used for this purpose is block structure models. Input–output Hammerstein structure referred to the classical Hammerstein model has been extended to new Hammerstein structure making use of states and inputs, hence called as input-state (IS) Hammerstein structure. In this paper it is shown that expansion of Taylor series leads to IS-Hammerstein structure. The accuracy of the approximation is improved by including higher order (second order) approximation. The input-state Hammerstein structure provides opportunities for model reduction in context of reducing the computational load by order reduction of states and Jacobians. IS-full order Hammerstein model has been implemented on a case study from the process industry namely the high purity distillation column. Within the operational domain of a process, the IS-Hammerstein structure provides a reduced order mode that can be used for online application purposes (i.e., optimization, model predictive control, etc.).