Nonlinear model predictive control based on multiple local linear models

Abstract

A long range nonlinear predictive control strategy using multiple local linear models is proposed. The multiple local linear models are identified through recurrent neuro-fuzzy network training. In this modelling approach, the process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to represent the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. Based upon the multiple local linear models, a nonlinear model based controller is developed by combining several local linear model based predictive controllers which usually have analytical solutions. Control actions obtained based on local incremental models contain inherent integral actions eliminating static offsets in a natural way. The techniques are demonstrated by applying to pH control in a continuous stirred tank reactor.