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Applied Control Systems Design pp 1–10Cite as

Introduction

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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.

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References

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Astrom, K.J., Eykhoff, P.: System identification—A survey. Automatica 7(2), 123–162 (1971)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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. 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. Burl, J.B.: Linear Optimal Control, 3rd edn. Prentice Hall, New York (1998)

    Google Scholar 

  16. Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making: Theory and Methodology. North-Holland (Elsevier), New York (1983)

    MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Ghiaus, C., Chicinas, A., Inard, C.: Grey-box identification of air-handling unit elements. Control Eng. Pract. 15(4), 421–433 (2007)

    Article  Google Scholar 

  23. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  25. Hsia, T.: System Identification: Least-Squares Methods. Lexington Books, Lexington (1977)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Kehoe, M.W.: A historical overview of flight flutter testing, NASA TR 4720, Oct. 1995

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  30. Miranda, V., Simpson, R.: Modelling and simulation of an industrial multiple-effect evaporator: Tomato concentrate. J. Food Eng. 66, 203–210 (2005)

    Article  Google Scholar 

  31. Neilsen, K.M., Pedersen, T.S., Nielsen, J.F.D.: Simulation and control of multieffect evaporator

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  33. Norgaard, M.: Neural network based system identification—TOOLBOX, Tech. Univ. Denmark, Lyngby, Tech. Rep. 97-E-851 (1997)

    Google Scholar 

  34. Ogata, K.: MATLAB for Control Engineers. Prentice-Hall, New York (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  36. Piroddi, L.: Simulation error minimization methods for NARX model identification. Int. J. Model. Identif. Control 3(4), 392–403 (2008)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  40. Roffel, B., Betlem, B.: Process Dynamics and Control. Wiley, London (2006)

    Google Scholar 

  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)

    Article  Google Scholar 

  42. Sinha, N.K., Kuszta, B.: Modelling and Identification of Dynamic Systems. Von-Nostrand Reinhold, New York (1983)

    Google Scholar 

  43. Sinha, N.K., Rao, G.P. (eds.): Identification of Continuous-Time Systems. Kluwer Academic, Dordrecht (1991)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  45. Soderstrom, T., Stoica, P.: System Identification. Prentice-Hall, New York (1989)

    Google Scholar 

  46. Stefanov, Z., Hoo, K.A.: Control of a multiple-effect falling-film evaporator plant. Ind. Eng. Chem. Res. 44, 3146–3158 (2005)

    Article  Google Scholar 

  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. Zwillinger, D.: Standard Mathematical Tables and Formulae, 31st edn. Chapman & Hall/CRC, Boca Raton (2002)

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Systems Engineering, King Fahad Univ. of Petroleum & Minerals, Dhahran, Saudi Arabia

    Magdi S. Mahmoud

  2. Dept. Automatic Control, Beijing Institute of Technology, Beijing, China, People’s Republic

    Prof. Yuanqing Xia

Authors
  1. Magdi S. Mahmoud
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  2. Prof. Yuanqing Xia
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© 2012 Springer-Verlag London Limited

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Mahmoud, M.S., Xia, Y. (2012). Introduction. In: Applied Control Systems Design. Springer, London. https://doi.org/10.1007/978-1-4471-2879-3_1

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  • DOI: https://doi.org/10.1007/978-1-4471-2879-3_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2878-6

  • Online ISBN: 978-1-4471-2879-3

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