Predictive control of nonlinear processes using interpolated models

Chemical processes are nonlinear and have been controlled using linear models. However, controllers based on linear models do not perform well for highly nonlinear situations. Several methods have been proposed to deal with the nonlinearity. Most of these methods are based on fundamental models, in the form of differential equations, that are difficult to obtain for industrial processes. In this paper, a procedure for handling the nonlinearity of industrial processes is presented which is based upon step response models that are easier to obtain. The step response models are obtained for a few sub-regions of the operating region experimentally and the models for other sub-regions are determined through interpolation. The approach is tested on example problems from the literature through simulations. The results show that a significant improvement in the control performance can be achieved in this manner.