Simplified algorithm for Laguerre approximationof URC networks

Morvan, R.; Tanguy, Noël; Vilbe, P.; Calvez, L.C.

doi:10.1049/el:19990929

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2005

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“…Fromĝ (s), the first N = 150 coefficients of a Laguerre model are recursively computed using [17] for two different weight functions ̟1 (t) = 1 and ̟2 (t) = e −30t . The corresponding Laguerre parameters (α1; γ1) = (18.5; 37.0) and (α2; γ2) = (8.95; 47.9) used for the computation of the truncated Laguerre spectrum ofĝ (s) came from the optimization method described in [16], [18].…”

Section: Numerical Examplementioning

confidence: 99%

Laguerre-Gram reduced-order modeling

Amghayrir

Tanguy

Brehonnet

et al. 2005

IEEE Trans. Automat. Contr.

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We present an efficient model reduction procedure based on the Laguerre description of the system to be approximated. Using a one-order operator defined in the Laplace domain we construct a pencil of functions and formulate the problem as the minimization of the L 2 ̟ R + criterion. The use of a weight function in the inner product definition allows a control of the time-error spreading in model reduction procedure. We show how the required Gram matrix can be computed efficiently and prove that the impulse response of the reduced model is also in L 2 ̟ R +. The transfer function approach allows an immediate and promising application in model reduction of infinite dimensional systems. An extension to MIMO systems is also given.

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Section: Numerical Examplementioning

confidence: 99%