Multi-step Ahead Prediction Analysis for MPC-relevant Models

Model predictive control (MPC) is one of the most successful controllers in industries and widely applied in petroleum refining and petrochemical processes. Its inherent model-based strategy, however, renders it sensitive to changes that occur when the plants operate outside the boundaries of its original operating conditions. In this paper, a nonlinear empirical model based on parallel orthonormal basis function-neural networks structure, which has been shown to be able to extend the applicable regions of the model, is evaluated for its multi-step ahead prediction capability and compared to the conventional neural networks models with different scaling procedures. It has been shown that the nonlinear model exhibited sufficient multi-step ahead prediction capability that renders it a promising candidate for MPC applications that can potentially improve the closed-loop control performance in extended regions and this is important in retaining the positive benefits of MPC in industries.