Simulation Model Emulation in Control System Design

  • C. X. Lu
  • N. W. Rees
  • Peter C. Young


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.


  1. 1.
    Åström, K.J., Bell, R.D.: Drum-boiler dynamics. Automatica 36, 363–378 (2000) MATHCrossRefGoogle Scholar
  2. 2.
    Beven, K.J., Young, P.C., Leedal, D.: Computationally efficient flood water level prediction (with uncertainty). In: Proceedings European Conference on Flood Risk Management, Oxford (2008) Google Scholar
  3. 3.
    Chotai, A., Young, P.C., McKenna, P.G., Tych, W.: Proportional-Integral-Plus (PIP) design for delta operator systems: Part 2, MIMO systems. Int. J. Control 70, 149–168 (1998) MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Goodwin, G.C., Payne, R.L.: Dynamic System Identification: Experiment Design and Data Analysis. Academic Press, New York (1977) MATHGoogle Scholar
  5. 5.
    Liaw, C.M.: Model reduction of discrete systems using the power decomposition method. Proc. Inst. Electr. Eng. D 133, 30–34 (1986) MATHGoogle Scholar
  6. 6.
    Lu, C.: Advanced control of power plant—a practical approach. PhD thesis, University of New South Wales, Australia (2009, submitted) Google Scholar
  7. 7.
    Lu, C., Rees, N., Donaldson, S.: The use of the Åström-Bell model for the design on drum level controllers in power plant boilers. In: Proceedings of the 16th IFAC World Congress (2005) Google Scholar
  8. 8.
    McDonald, J., Kwatny, H.: Design and analysis of boiler turbine generator controls using optimal linear regulator theory. IEEE Trans. Autom. Control 18(3), 202–209 (1973) CrossRefGoogle Scholar
  9. 9.
    Rees, N.: Advanced power plant control for large load changes and disturbances. In: IFAC/CIGRE Symposium on Control of Power Systems and Power Plants, pp. 641–649 (1997) Google Scholar
  10. 10.
    Rees, N., Lu, C.: Some thoughts on the advanced control of electrical power plants. Trans. Inst. Meas. Control 24(2), 87–106 (2002) CrossRefGoogle Scholar
  11. 11.
    Taylor, C.J., Chotai, A., Young, P.C.: State space control system design based on non-minimal state variable feedback: further generalization and unification results. Int. J. Control 73, 1329–1345 (2000) MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Young, P.C.: Recursive Estimation and Time-Series Analysis. Springer, Berlin (1984). New revised edition in preparation for publication in 2011 MATHGoogle Scholar
  13. 13.
    Young, P.C.: Data-based mechanistic modelling, generalised sensitivity and dominant mode analysis. Comput. Phys. Commun. 117, 113–129 (1999) CrossRefGoogle Scholar
  14. 14.
    Young, P.C.: Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation. In: Fitzgerald, W.J., Walden, A., Smith, R., Young, P.C. (eds.) Nonlinear and Nonstationary Signal Processing, pp. 74–114. Cambridge University Press, Cambridge (2000) Google Scholar
  15. 15.
    Young, P.C.: The identification and estimation of nonlinear stochastic systems. In: Mees, A.I. (ed.) Nonlinear Dynamics and Statistics, pp. 127–166. Birkhäuser, Boston (2001) Google Scholar
  16. 16.
    Young, P.C.: The refined instrumental variable method: unified estimation of discrete and continuous-time transfer function models. J. Eur. Syst. Autom. 42, 149–179 (2008) Google Scholar
  17. 17.
    Young, P.C., Castelletti, A., Pianosi, F.: The data-based mechanistic approach in hydrological modelling. In: Castelletti, A., Sessa, R.S. (eds.) Topics on System Analysis and Integrated Water Resource Management, pp. 27–48. Elsevier, Amsterdam (2007) Google Scholar
  18. 18.
    Young, P.C., Chotai, A., McKenna, P.G., Tych, W.: Proportional-Integral-Plus (PIP) design for delta operator systems: Part 1, SISO systems. Int. J. Control 70, 123–147 (1998) MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Young, P.C., Leedal, D., Beven, K.J.: Reduced order emulation of distributed hydraulic models. In: Proceedings 15th IFAC Symposium on System Identification SYSID09, St Malo, France (2009) Google Scholar
  20. 20.
    Young, P.C., McKenna, P., Bruun, J.: Identification of nonlinear stochastic systems by state dependent parameter estimation. Int. J. Control 74, 1837–1857 (2001) MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Young, P.C., Parkinson, S.: Simplicity out of complexity. In: Beck, M.B. (ed.) Environmental Foresight and Models: A Manifesto, pp. 251–294. Elsevier, Oxford (2002) CrossRefGoogle Scholar
  22. 22.
    Young, P.C., Parkinson, S., Lees, M.J.: Simplicity out of complexity: Occam’s razor revisited. J. Appl. Stat. 23, 165–210 (1996) CrossRefGoogle Scholar
  23. 23.
    Young, P.C., Ratto, M.: A unified approach to environmental systems modeling. Stoch. Environ. Res. Risk Assess. 23, 1037–1057 (2009) CrossRefGoogle Scholar
  24. 24.
    Young, P.C., Ratto, M.: Statistical emulation of large linear dynamic models. Technometrics 53, 29–43 (2011) MathSciNetCrossRefGoogle Scholar

Copyright information

© 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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