Simulation Model Emulation in Control System Design


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.


Reduce Order Model Emulation Model Control System Design Boiler System Present Chapter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Part of this project was carried out during Peter Young’s visits to the UNSW as Visiting Professor. He is grateful for the financial assistance received on these visits.


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© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.School of Electrical Engineering and TelecommunicationsUniversity of New South WalesSydneyAustralia
  2. 2.Systems and Control Group, Lancaster Environment CentreLancaster UniversityLancasterUK
  3. 3.Fenner School of Environment and SocietyAustralian National UniversityCanberraAustralia

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