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Introduction

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Identification of Dynamic Systems

Abstract

The temporal behavior of systems, such as e.g. technical systems from the areas of electrical engineering, mechanical engineering, and process engineering, as well as non-technical systems from areas as diverse as biology, medicine, chemistry, physics, economics, to name a few, can uniformly be described by mathematical models. This is covered by systems theory. However, the application of systems theory requires that the mathematical models for the static and dynamic behavior of the systems and their elements are known. The process of setting up a suitable model is called modeling. As is shown in the following section, two general approaches to modeling exist, namely theoretical and experimental modeling, both of which have their distinct advantages and disadvantages.

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References

  • Åström KJ, Eykhoff P (1971) System identification – a survey. Automatica 7(2):123–162

    Article  MATH  Google Scholar 

  • Åström KJ, Wittenmark B (1997) Computer controlled systems: theory and design, 3rd edn. Prentice-Hall information and system sciences series, Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Åström KJ, Wittenmark B (2008) Adaptive control, 2nd edn. Dover Publications, Mineola, NY

    Google Scholar 

  • Åström KJ, Goodwin GC, Kumar PR (1995) Adaptive control, filtering, and signal processing. Springer, New York

    MATH  Google Scholar 

  • Bobál V, Böhm J, Fessl J, Machácek J (2005) Digital self-tuning controllers — Algorithms, implementation and applications. Advanced Textbooks in Control and Signal Processing, Springer, London

    Google Scholar 

  • Bohlin T (2006) Practical grey-box process identification: Theory and applications. Advances in Industrial Control, Springer, London

    Google Scholar 

  • van den Bos A (2007) Parameter estimation for scientists and engineers. Wiley-Interscience, Hoboken, NJ

    MATH  Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (2008) Time series analysis: Forecasting and control, 4th edn. Wiley Series in Probability and Statistics, John Wiley, Hoboken, NJ

    MATH  Google Scholar 

  • Brammer K, Siffling G (1994) Kalman-Bucy-Filter: Deterministische Beobachtung und stochastische Filterung, 4th edn. Oldenbourg, München

    Google Scholar 

  • Chui CK, Chen G (2009) Kalman filtering with real-time applications, 4th edn. Springer, Berlin

    Google Scholar 

  • Crowe J, Chen GR, Ferdous R, Greenwood DR, Grimble MJ, Huang HP, Jeng JC, Johnson MA, Katebi MR, Kwong S, Lee TH (2005) PID control: New identification and design methods. Springer, London

    Google Scholar 

  • DIN Deutsches Institut für Normung e V (Juli 1998) Begriffe bei Prozessrechensystemen

    Google Scholar 

  • Doyle FJ, Pearson RK, Ogunnaike BA (2002) Identification and control using Volterra models. Communications and Control Engineering, Springer, London

    Google Scholar 

  • Eubank RL (2006) A Kalman filter primer, Statistics, textbooks and monographs, vol 186. Chapman and Hall/CRC, Boca Raton, FL

    Google Scholar 

  • Eykhoff P (1994) Identification inMeasurement and Instrumentation. In: Finkelstein K, Grattam TV (eds) Concise Encyclopaedia of Measurement and Instrumentation, Pergamon Press, Oxford, pp 137–142

    Google Scholar 

  • Garnier H, Wang L (2008) Identification of continuous-time models from sampled data. Advances in Industrial Control, Springer, London

    Book  Google Scholar 

  • Goodwin GC, Doná JA, Seron MM (2005) Constrained control and estimation: An optimisation approach. Communications and Control Engineering, Springer, London

    Google Scholar 

  • Grewal MS, Andrews AP (2008) Kalman filtering: Theory and practice using MATLAB, 3rd edn. John Wiley & Sons, Hoboken, NJ

    Google Scholar 

  • Harris C, Hong X, Gan Q (2002) Adaptive modelling, estimation and fusion from data: A neurofuzzy approach. Advanced information processing, Springer, Berlin

    Google Scholar 

  • Heij C, Ran A, Schagen F (2007) Introduction to mathematical systems theory : linear systems, identification and control. Birkhäuser Verlag, Basel

    MATH  Google Scholar 

  • Ikonen E, Najim K (2002) Advanced process identification and control, Control engineering, vol 9. Dekker, New York

    Google Scholar 

  • Isermann R (1991) Digital control systems, 2nd edn. Springer, Berlin

    Google Scholar 

  • Isermann R (1992) Identifikation dynamischer Systeme: Grundlegende Methoden (Vol. 1). Springer, Berlin

    MATH  Google Scholar 

  • Isermann R (2005) Mechatronic Systems: Fundamentals. Springer, London

    Google Scholar 

  • Isermann R (2006) Fault-diagnosis systems: An introduction from fault detection to fault tolerance. Springer, Berlin

    Google Scholar 

  • Isermann R, Lachmann KH, Matko D (1992) Adaptive control systems. Prentice Hall international series in systems and control engineering, Prentice Hall, New York, NY

    Google Scholar 

  • Juang JN (1994) Applied system identification. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  • Juang JN, Phan MQ (2006) Identification and control of mechanical systems. Cambridge University Press, Cambridge

    Google Scholar 

  • Kamen EW, Su JK (1999) Introduction to optimal estimation. Advanced Textbooks in Control and Signal Processing, Springer, London

    Google Scholar 

  • Koch KR (1999) Parameter estimation and hypothesis testing in linear models, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  • Lewis FL, Xie L, Popa D (2008) Optimal and robust estimation: With an introduction to stochastic control theory, Automation and control engineering, vol 26, 2nd edn. CRC Press, Boca Raton, FL

    Google Scholar 

  • Ljung L (1999) System identification: Theory for the user, 2nd edn. Prentice Hall Information and System Sciences Series, Prentice Hall PTR, Upper Saddle River, NJ

    Google Scholar 

  • Mikleš J, Fikar M (2007) Process modelling, identification, and control. Springer, Berlin

    MATH  Google Scholar 

  • Nelles O (2001) Nonlinear system identification: From classical approaches to neural networks and fuzzy models. Springer, Berlin

    MATH  Google Scholar 

  • O’Dwyer A (2009) Handbook of PI and PID controller tuning rules, 3rd edn. Imperial College Press, London

    Book  Google Scholar 

  • van Overshee P, de Moor B (1996) Subspace identification for linear systems: Theory – implementation – applications. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Pfeiffer BM, Wieser R, Lorenz O (2009) Wie verbessern Sie die Performance Ihrer Anlage mit Hilfe der passenden APC-Funktionen? Teil 1: APC-Werkzeuge in Prozessleitsystemen. atp 51(4):36–44

    Google Scholar 

  • Pintelon R, Schoukens J (2001) System identification: A frequency domain approach. IEEE Press, Piscataway, NJ

    Book  Google Scholar 

  • Raol JR, Girija G, Singh J (2004) Modelling and parameter estimation of dynamic systems, IEE control engineering series, vol 65. Institution of Electrical Engineers, London

    Google Scholar 

  • Sastry S, Bodson M (1989) Adaptive control : stability, convergence, and robustness. Prentice Hall Information and System Sciences Series, Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  • Söderström T (2002) Discrete-time stochastic systems: Estimation and control, 2nd edn. Advanced Textbooks in Control and Signal Processing, Springer, London

    MATH  Google Scholar 

  • Unbehauen H (2000) Regelungstechnik Bd. 3: Identifikation, Adaption, Optimierung, 6th edn. Vieweg + Teubner, Wiesbaden

    Google Scholar 

  • Verhaegen M, Verdult V (2007) Filtering and system identification: A least squares approach. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Zadeh LA (1962) From circuit theory to system theory. Proc IRE 50:850–865

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technische Universität Darmstadt, Institut für Automatisierungstechnik, Landgraf-Georg-Straße 4, 64283, Darmstadt, Germany

    Prof. Dr. -Ing. Dr. h.c. Rolf Isermann & Dr. -Ing. Marco Münchhof M.S./SUNY

Authors
  1. Prof. Dr. -Ing. Dr. h.c. Rolf Isermann
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  2. Dr. -Ing. Marco Münchhof M.S./SUNY
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Correspondence to Rolf Isermann .

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© 2011 Springer Berlin Heidelberg

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Isermann, R., Münchhof, M. (2011). Introduction. In: Identification of Dynamic Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78879-9_1

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  • DOI: https://doi.org/10.1007/978-3-540-78879-9_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78878-2

  • Online ISBN: 978-3-540-78879-9

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