Titlebook: Subspace Identification for Linear Systems; Theory — Implementat Peter Overschee,Bart Moor Book 1996 Kluwer Academic Publishers 1996 Matlab

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https://doi.org/10.1007/978-1-4613-0465-4Matlab; Signal; algebra; algorithm; dynamical systems; manufacturing; mechatronics; model; optimal control; p

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Deterministic Identification,In this Chapter we treat the subspace identification of purely deterministic systems, with no measurement nor process noise (vk ≡ wk ≡ 0 in Figure 1.4). We treat this problem for two reasons:

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978-1-4613-8061-0Kluwer Academic Publishers 1996

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Combined Deterministic-Stochastic Identification,d the process and measurement noise w. and v. are different from zero. We use the results and ideas of the two previous Chapters to derive the main Theorem, which indicates how the combined deterministic-stochastic Kalman filter states can be extracted from the input-output data.

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sights. .The .implementation. of subspace identification algorithms isdiscussed in terms of the robust and computationally efficient RQ andsingular value decompositions, which are well-established algorithmsfrom numerical linear algebra. The algorithms are implemented incombination with a whole set of classic978-1-4613-8061-0978-1-4613-0465-4