Abstract
The correlation methods for single periodic test signals, which have been described in Chap. 5 provide only one discrete point of the frequency response at each measurement with one measurement frequency. At the start of each experiment, one must wait for the decay of the transients. Due to these circumstances, the methods are not suitable for online identification in real time. Thus, it is interesting to employ test signals which have a broad frequency spectrum and thus excite more frequencies at once as did the non-periodic deterministic test signals.
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References
Bendat JS, Piersol AG (2010) Random data: Analysis and measurement procedures, 4th edn. Wiley-Interscience, New York
Chow P, Davies AC (1964) The synthesis of cyclic code generators. Electron Eng 36:253–259
Cummins JC (1964) A note on errors and signal to noise ratio of binary crosscorrelation measurements of system impulse response. Atom Energy Establishment, Winfrith (AEEW), Dorset
Davies WDT (1970) System identification for self-adaptive control. Wiley-Interscience, London
Doyle FJ, Pearson RK, Ogunnaike BA (2002) Identification and control using Volterra models. Communications and Control Engineering, Springer, London
Eykhoff P (1964) Process parameter estimation. Progress in Control Engineering 2:162–206
Godman TP, Reswick JB (1956) Determination of th system characteristics from normal operation modes. Trans ASME 78:259–271
Hänsler E (2001) Statistische Signale: Grundlagen und Anwendungen. Springer, Berlin
Hughes M, Norton A (1962) The measurement of control system characteristics by means of cross-correlator. Proc IEE Part B 109(43):77–83
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
Papoulis A (1962) The Fourier integral and its applications. McGraw Hill, New York
Pearson RK (1999) Discrete-time dynamic models. Topics in chemical engineering, Oxford University Press, New York
Pintelon R, Schoukens J (2001) System identification: A frequency domain approach. IEEE Press, Piscataway, NJ
Rödder P (1973) Systemidentifikation mit stochastischen Signalen im geschlossenen Regelkreis – Verfahren der Fehlerabschätzung. Dissertation. RWTH Aachen, Aachen
Rödder P (1974) Nichtbeachtung der Rückkopplung bei der Systemanalyse mit stochastischen Signalen. Regelungstechnik 22:154–156
Sage AP, Melsa JL (1971) System identification. Academic Press, New York
Solodownikow WW (1964) Einführung in die statistische Dynamik linearer Regelsysteme. Oldenbourg Verlag, München
Tulleken HJAF (1990) Generalized binary noise test-signal concept for improved identification-experiment design. Automatica 26(1):37–49
Zimmerschied R (2002) Entwurf von Anregungssignalen für die Identifikation nichtlinearer dynamischer Prozesse. Diplomarbeit. Institut für Regelungstechnik, TU Darmstadt, Darmstadt
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Isermann, R., Münchhof, M. (2011). Correlation Analysis with Continuous Time Models. In: Identification of Dynamic Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78879-9_6
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DOI: https://doi.org/10.1007/978-3-540-78879-9_6
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