Abstract
The choice of an appropriate model structure plays an important role in the identification of MIMO systems as it determines the number of parameters, the convergence and the computational effort. Hence, in this chapter, different model structures for MIMO systems will be presented.
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References
Ackermann J (1988) Abtastregelung, 3rd edn. Springer, Berlin
Blessing P (1979) Identification of the input/output and noise-dyanmics of linear multi-variable systems. In: Proceedings of the 5th IFAC Symposium on Identification and System Parameter Estimation Darmstadt, Pergamon Press, Darmstadt, Germany
Blessing P (1980) Ein Verfahren zur Identifikation von linearen, stochastisch gestörten Mehrgrößensystemen: KfK-PDV-Bericht. Kernforschungszentrum Karlsruhe, Karlsruhe
Brauer A (1953) On a new class of Hadamard determinants. Math Z 58(1):219–225
Briggs PAN, Godfrey KR, Hammond PH (1967) Estimation of process dynamic characteristics by correlation methods using pseudo random signals. In: Proceedings of the IFAC Symposium Identification, Prag, Czech Republic
Gevers M, Miskovic L, Bonvin D, Karimi A (2006) Identification of multi-input systems: Variance analysis and input design issues. Automatica 42(4):559–572
Goodwin GC, Sin KS (1984) Adaptive filtering, prediction and control. Prentice-Hall information and system sciences series, Prentice-Hall, Englewood Cliffs, NJ
Guidorzi R (1975) Canonical structures in the identification of multivariable systems. Automatica 11(4):361–374
Hensel H (1987) Methoden des rechnergestützten Entwurfs und Echtzeiteinsatzes zeitdiskreter Mehrgrößenregelungen und ihre Realisierung in einem CAD-System. Fortschr.-Ber. VDI Reihe 20 Nr. 4. VDI Verlag, Düsseldorf
Ho BL, Kalman RE (1966) Effective construction of linear state variable models from input/output functions. Regelungstechnik 14:545–548
Isermann R (1991) Digital control systems, 2nd edn. Springer, Berlin
Juang JN (1994) Applied system identification. Prentice Hall, Englewood Cliffs, NJ
Pintelon R, Schoukens J (2001) System identification: A frequency domain approach. IEEE Press, Piscataway, NJ
Popov VM (1972) Invariant description of linear time-variant controllable systems. SIAM J Control 10:252–264
Schreiber A, Isermann R (2009) Methods for stationary and dynamic measurement and modeling of combustion engines. In: Proceedings of the 3rd International Symposium on Development Methodology, Wiesbaden, Germany
Schumann R (1982) Digitale parameteradaptive Mehrgrößenregelung - KfK-PDVBericht Nr. 217. Kernforschungszentrum Karlsruhe, Karlsruhe
Schwarz H (1967, 1971) Mehrfach-Regelungen, vol 1. Springer, Berlin
Tsafestas SG (1977) Multivariable control system identification using pseudo random test input. Int J Control Theory and Applic 5:58–66
Woodside CM (1971) Estimation of the order of linear systems. Automatica 7(6):727–733
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Isermann, R., Münchhof, M. (2011). Parameter Estimation for MIMO Systems. In: Identification of Dynamic Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78879-9_17
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DOI: https://doi.org/10.1007/978-3-540-78879-9_17
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