Advertisement

Introduction

  • Magdi S. Mahmoud
  • Yuanqing Xia

Abstract

In this chapter, we provide an overview of the two fundamental subjects: systems identification and control design. System identification embodies powerful techniques for building models of complex systems in communications, signal processing, control, and other engineering disciplines.

This textbook adopts an information-based approach to control system design. Therefore, the main goal is to give the necessary pool of knowledge for the comprehension and implementation of applied techniques for system identification and control design. These techniques are applicable to various types of industrial processes. The book has been written taking into account the needs of the designer and the user of such systems. Theoretical developments that are not directly relevant to the design have been omitted. The book also takes into account the availability of dedicated control software.

Keywords

Process Industry Model Predictive Control Distillation Column Distribute Control System System Identification Method 
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.

References

  1. 1.
    Abdelazim, T., Malik, O.: Identification of nonlinear systems by Takagi-Sugeno fuzzy logic grey box modeling for real-time control. Control Eng. Pract. 13(12), 1489–1498 (2005) CrossRefGoogle Scholar
  2. 2.
    Aguirre, L.A.: A nonlinear correlation function for selecting the delay time in dynamical reconstructions. Phys. Lett. 203A(2–3, 88–94 (1995) Google Scholar
  3. 3.
    Aguirre, L.A., Donoso-Garcia, P.F., Santos-Filho, R.: Use of a priori information in the identification of global nonlinear models—A case study using a buck converter. IEEE Trans. Circuits Syst. I, Regul. Pap. 47(7), 1081–1085 (2000) CrossRefGoogle Scholar
  4. 4.
    Aguirre, L.A., Barroso, M.F.S., Saldanha, R.R., Mendes, E.M.A.M.: Imposing steady-state performance on identified nonlinear polynomial models by means of constrained parameter estimation. IEE Proc. Part D. Control Theory Appl. 151(2), 174–179 (2004) CrossRefGoogle Scholar
  5. 5.
    Aguirre, L.A., Coelho, M.C.S., Corrêa, M.V.: On the interpretation and practice of dynamical differences between Hammerstein and Wiener models. IEE Proc. Part D. Control Theory Appl. 152(4), 349–356 (2005) CrossRefGoogle Scholar
  6. 6.
    Astrom, K.J., Eykhoff, P.: System identification—A survey. Automatica 7(2), 123–162 (1971) MathSciNetCrossRefGoogle Scholar
  7. 7.
    Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proc. 2nd Int. Conf. Genetic Algorithms Genetic Algorithms Their Appl., Mahwah, N.J., pp. 14–21 (1987) Google Scholar
  8. 8.
    Bakker, H.H.C., Marsh, C., Paramalingam, S., Chen, H.: Cascade controller design for concentration in a falling film evaporators. Food Control 17(5), 325–330 (2006) CrossRefGoogle Scholar
  9. 9.
    Barbosa, B.H.: Instrumentation, modelling, control and supervision of a hydraulic pumping system and turbine–generator module (in Portuguese). Master’s thesis, Sch. Elect. Eng., Federal Univ. Minas Gerais, Belo Horizonte, Brazil (2006) Google Scholar
  10. 10.
    Barroso, M.S.F., Takahashi, R.H.C., Aguirre, L.A.: Multi-objective parameter estimation via minimal correlation criterion. J. Process Control 17(4), 321–332 (2007) CrossRefGoogle Scholar
  11. 11.
    Billings, S.A., Voon, W.S.F.: Least squares parameter estimation algorithms for nonlinear systems. Int. J. Syst. Sci. 15(6), 601–615 (1984) MathSciNetMATHGoogle Scholar
  12. 12.
    Billings, S.A., Chen, S., Korenberg, M.J.: Identification of MIMO nonlinear systems using a forward-regression orthogonal estimator. Int. J. Control 49(6), 2157–2189 (1989) MathSciNetMATHGoogle Scholar
  13. 13.
    Bingulac, S., Sinha, N.K.: On the identification of continuous-time systems from the samples of input–output data. In: Proc. Seventh Int. Conf. on Mathematical and Computer Modeling, Chicago, IL, pp. 231–239 (1989) Google Scholar
  14. 14.
    Bucharles, A., Cassan, H., Roubertier, J.: Advanced parameter identification techniques for near real = time flight flutter test analysis. AIAA, Paper 90-1275, May 1990 Google Scholar
  15. 15.
    Burl, J.B.: Linear Optimal Control, 3rd edn. Prentice Hall, New York (1998) Google Scholar
  16. 16.
    Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making: Theory and Methodology. North-Holland (Elsevier), New York (1983) MATHGoogle Scholar
  17. 17.
    Chen, S., Billings, S.A., Luo, W.: Orthogonal least squares methods and their application to nonlinear system identification. Int. J. Control 50(5), 1873–1896 (1989) MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Connally, P., Li, K., Irwing, G.W.: Prediction and simulation error based perceptron training: Solution space analysis and a novel combined training scheme. Neurocomputing 70, 819–827 (2007) CrossRefGoogle Scholar
  19. 19.
    Cooper, J.: Parameter estimation methods for the flight flutter testing. In: Proc. the 80th AGARD Structures and Materials Panel, CP-566, AGARD, Rotterdam, The Netherlands, 1995 Google Scholar
  20. 20.
    Correa, M.V., Aguirre, L.A., Saldanha, R.R.: Using steady-state prior knowledge to constrain parameter estimates in nonlinear system identification. IEEE Trans. Circuits Syst. I, Regul. Pap. 49(9), 1376–1381 (2002) CrossRefGoogle Scholar
  21. 21.
    Cunningham, P., Canty, N., O’Mahony, T., O’Connor, B., O’Callagham, D.: System identification of a falling film evaporator in the dairy industry. In: Proc. of SYSID’94, Copenhagen, Denmark, vol. 1, 234–239 (1994) Google Scholar
  22. 22.
    Ghiaus, C., Chicinas, A., Inard, C.: Grey-box identification of air-handling unit elements. Control Eng. Pract. 15(4), 421–433 (2007) CrossRefGoogle Scholar
  23. 23.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989) MATHGoogle Scholar
  24. 24.
    Hsia, T.C.: On sampled-data approach to parameter identification of continuous-time linear systems. IEEE Trans. Autom. Control AC-17, 247–249 (1972) CrossRefGoogle Scholar
  25. 25.
    Hsia, T.: System Identification: Least-Squares Methods. Lexington Books, Lexington (1977) Google Scholar
  26. 26.
    Jakubek, S., Hametner, C., Keuth, N.: Total least squares in fuzzy system identification: An application to an industrial engine. Eng. Appl. Artif. Intell. 21, 1277–1288 (2008) CrossRefGoogle Scholar
  27. 27.
    Karimi, M., Jahanmiri, A.: Nonlinear modeling and cascade control design for multieffect falling film evaporator. Iran. J. Chem. Eng. 3(2) (2006) Google Scholar
  28. 28.
    Kehoe, M.W.: A historical overview of flight flutter testing, NASA TR 4720, Oct. 1995 Google Scholar
  29. 29.
    Leontaritis, I.J., Billings, S.A.: Input–output parametric models for nonlinear systems. Part II: Deterministic nonlinear system. Int. J. Control 41(2), 329–344 (1985) MathSciNetMATHCrossRefGoogle Scholar
  30. 30.
    Miranda, V., Simpson, R.: Modelling and simulation of an industrial multiple-effect evaporator: Tomato concentrate. J. Food Eng. 66, 203–210 (2005) CrossRefGoogle Scholar
  31. 31.
    Neilsen, K.M., Pedersen, T.S., Nielsen, J.F.D.: Simulation and control of multieffect evaporator Google Scholar
  32. 32.
    Nepomuceno, E.G., Takahashi, R.H.C., Aguirre, L.A.: Multiobjective parameter estimation: Affine information and least-squares formulation. Int. J. Control 80(6), 863–871 (2007) MathSciNetMATHCrossRefGoogle Scholar
  33. 33.
    Norgaard, M.: Neural network based system identification—TOOLBOX, Tech. Univ. Denmark, Lyngby, Tech. Rep. 97-E-851 (1997) Google Scholar
  34. 34.
    Ogata, K.: MATLAB for Control Engineers. Prentice-Hall, New York (2008) Google Scholar
  35. 35.
    Pan, Y., Lee, J.H.: Modified subspace identification for long-range prediction model for inferential control. Control Eng. Pract. 16(12), 1487–1500 (2008) CrossRefGoogle Scholar
  36. 36.
    Piroddi, L.: Simulation error minimization methods for NARX model identification. Int. J. Model. Identif. Control 3(4), 392–403 (2008) CrossRefGoogle Scholar
  37. 37.
    Piroddi, L., Spinelli, W.: An identification algorithm for polynomial NARX-models based on simulation error minimization. Int. J. Control 76(17), 1767–1781 (2003) MathSciNetMATHCrossRefGoogle Scholar
  38. 38.
    Quaak, P., van Wijck, M.P.C.M., van Haren, J.J.: Comparison of process identification and physical modeling for falling film evaporators. Food Control 5(2), 73–82 (1994) CrossRefGoogle Scholar
  39. 39.
    Rangaiah, G., Saha, P., Tade, M.: Nonlinear model predictive control of an industrial four-stage evaporator system via simulation. Chem. Eng. J. 87, 285–299 (2002) CrossRefGoogle Scholar
  40. 40.
    Roffel, B., Betlem, B.: Process Dynamics and Control. Wiley, London (2006) Google Scholar
  41. 41.
    Sinha, N.K.: Estimation of transfer function of continuous-time systems from samples of input–output data. Proc. Inst. Electr. Eng. 119, 612–614 (1972) CrossRefGoogle Scholar
  42. 42.
    Sinha, N.K., Kuszta, B.: Modelling and Identification of Dynamic Systems. Von-Nostrand Reinhold, New York (1983) Google Scholar
  43. 43.
    Sinha, N.K., Rao, G.P. (eds.): Identification of Continuous-Time Systems. Kluwer Academic, Dordrecht (1991) MATHGoogle Scholar
  44. 44.
    Sjoberg, J., Zhang, Q., Ljung, L., Beneviste, A., Delyon, B., Glorennec, P., Hjalmarsson, H., Juditsky, A.: Non-linear black-box modeling in system identification: A unified overview. Automatica 31, 31–1961 (1995) CrossRefGoogle Scholar
  45. 45.
    Soderstrom, T., Stoica, P.: System Identification. Prentice-Hall, New York (1989) Google Scholar
  46. 46.
    Stefanov, Z., Hoo, K.A.: Control of a multiple-effect falling-film evaporator plant. Ind. Eng. Chem. Res. 44, 3146–3158 (2005) CrossRefGoogle Scholar
  47. 47.
    Van Wijck, M.P., Quaak, P., van Haren, J.J.: Multivariable supervisory control of a four-effect falling film evaporator. Food Control 5(2), 234–243 (1994) Google Scholar
  48. 48.
    Zwillinger, D.: Standard Mathematical Tables and Formulae, 31st edn. Chapman & Hall/CRC, Boca Raton (2002) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Magdi S. Mahmoud
    • 1
  • Yuanqing Xia
    • 2
  1. 1.Department of Systems EngineeringKing Fahad Univ. of Petroleum & MineralsDhahranSaudi Arabia
  2. 2.Dept. Automatic ControlBeijing Institute of TechnologyBeijingChina, People’s Republic

Personalised recommendations