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Simulation Setup

  • Paweł D. DomańskiEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 245)

Abstract

The idea of this part is to compare selected and representative methods with the simulation benchmarks. Three areas of the industrial control are covered and reviewed in the chapter: assessment measures and methodology, considered control algorithms and assessed processes and control philosophies. The most representative measures are selected and validated with different SISO and MIMO simulated processes. These experiments address two main control algorithms used in process industry, i.e. PID and Model Predictive Control (MPC).

References

  1. 1.
    Adam, E.J., Marchetti, J.L.: Designing and tuning robust feedforward controllers. Comput. Chem. Eng. 28(9), 1899–1911 (2004)CrossRefGoogle Scholar
  2. 2.
    Åström, K.J., Hägglund, T.: New tuning methods for PID controllers. In: Proceedings 3rd European Control Conference, pp. 2456–2462 (1995)Google Scholar
  3. 3.
    Åström, K.J., Hägglund, T.: Benchmark systems for pid control. In: IFAC Digital Control: Past, Present and Future of PlD Control, pp. 165–166 (2000)CrossRefGoogle Scholar
  4. 4.
    Bequette, B.: Process Control: Modeling, Design, and Simulation, 1st edn. Prentice Hall Press, Upper Saddle River, NJ, USA (2002)Google Scholar
  5. 5.
    Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London, UK (1999)CrossRefGoogle Scholar
  6. 6.
    Clarke, W., Mohtadi, C., Tuffs, P.S.: Generalized predictive control—I. the basic algorithm. Automatica 23(2), 137–148 (1987a)CrossRefGoogle Scholar
  7. 7.
    Clarke, W., Mohtadi, C., Tuffs, P.S.: Generalized predictive control—II. extensions and interpretations. Automatica 23(2), 149–160 (1987b)CrossRefGoogle Scholar
  8. 8.
    Cutler, R., Ramaker, B.: Dynamic matrix control—a computer control algorithm. In: Proceedings AIChE National Meeting, Houston, TX, US (1979)Google Scholar
  9. 9.
    Ferreau, H.J., Almér, S., Peyrl, H., Jerez, J.L., Domahidi, A.: Survey of industrial applications of embedded model predictive control. In: 2016 European Control Conference, pp. 601–601 (2016)Google Scholar
  10. 10.
    Forbes, M.G., Patwardhan, R.S., Hamadah, H., Gopaluni, R.B.: Model predictive control in industry: challenges and opportunities. IFAC-PapersOnLine 48(8), 531–538, 9th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2015 (2015)CrossRefGoogle Scholar
  11. 11.
    Jeng, J.C., Lee, M.W.: Identification and controller tuning of cascade control systems based on closed-loop step responses. IFAC Proc. 45(15), 414–419, 8th IFAC Symposium on Advanced Control of Chemical Processes (2012)CrossRefGoogle Scholar
  12. 12.
    Kalman, R.E.: Contribution to the theory of optimal control. Boletin de la Sociedad Mathematica Mexicana 5, 102–119 (1960)MathSciNetGoogle Scholar
  13. 13.
    Lee, J.H.: Model predictive control: review of the three decades of development. Int. J. Control Autom. Syst. 9(3), 415 (2011)CrossRefGoogle Scholar
  14. 14.
    Maciejowski, J.M.: Predictive Control With Constraints. Prentice Hall, Harlow (2002)Google Scholar
  15. 15.
    Mehta, U., Majhi, S.: On-line identification of cascade control systems based on half limit cycle data. ISA Trans. 50(3), 473–478 (2011)CrossRefGoogle Scholar
  16. 16.
    Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Eng. Pract. 11, 733–764 (2003)CrossRefGoogle Scholar
  17. 17.
    Richalet, J., Rault, A., Testud, J.L., Papon, J.: Model algorithmic control of industrial processes. IFAC Proc. 10(16), 103–120, preprints of the 5th IFAC/IFIP International Conference on Digital Computer Applications to Process Control, The Hague, The Netherlands, 14–17 June, 1977Google Scholar
  18. 18.
    Tatjewski, P.: Advanced Control of Industrial Processes, Structures and Algorithms. Springer, London (2007)Google Scholar
  19. 19.
    Wood, R.K., Berry, M.W.: Terminal composition control of a binary distillation column. Chem. Eng. Sci. 28(9), 1707–1717 (1973)CrossRefGoogle Scholar
  20. 20.
    Yamamoto, S., Hashimoto, I.: Present status and future needs: the view from Japanese industry. In: Proceedings 4th International Conference on Chemical Process Control, pp. 1–28 (1991)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

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