Simulation Model Emulation in Control System Design

Abstract

Complex industrial processes are most often investigated by the formulation of mathematical models that combine the various static and dynamic unit processes into a computer-based ‘simulation model’ that is normally very large, with many parameters characterising numerous, interconnected linear and nonlinear components. By contrast, the multifarious model-based methods available for the design of control systems for such processes are normally based on much simpler models that reflect the ‘dominant modal’ behaviour of the system and so they cannot be applied directly to the large simulation model. This has given rise to a large literature on methods of ‘dynamic model reduction’, where a reduced order representation of the high order simulation model is obtained in various different ways. This chapter considers a recent approach of this kind, where the reduced order ‘emulation model’ is inferred by the application of advanced statistical identification and estimation methods to data obtained from planned experiments on the large simulation model. The practical utility of this Data-Based Mechanistic (DBM) approach to emulation modelling is illustrated by its application to the modelling and control of a multivariable, electrical power generation process. In this case, the ‘nominal’ emulation model is identified as a third order, three input-three output transfer function model and this is used as the basis for the successful Proportional-Integral-Plus (PIP) multivariable control of the large simulation model.