NDLI: Convex passivity enforcement of linear macromodels via alternate subgradient iterations

Content Provider	IEEE Xplore Digital Library
Author	Chinea, A. Grivet-Talocia, S. Calafiore, G.C.
Copyright Year	2012
Description	Author affiliation: IdemWorks s.r.l., C. Trento 13, 10129 Torino, Italy (Chinea, A.) \|\| Politecnico di Torino, C. Duca degli Abruzzi 24, 10129 Torino, Italy (Grivet-Talocia, S.; Calafiore, G.C.)
Abstract	This paper introduces a new algorithm for passivity enforcement of linear lumped macromodels in scattering form. As typical in most state of the art passivity enforcement methods, we start with an initial non-passive macromodel obtained by a Vector Fitting process, and we perturb its parameters to make it passive. The proposed scheme is based on a convex formulation of both passivity constraints and objective function for accuracy preservation, thus allowing a formal proof of convergence to the unique optimal passive macromodel. This is a distinctive feature that differentiates the new scheme with respect to most state of the art methods, which either do not guarantee convergence or are not able to provide the most accurate solution. The presented algorithm can thus be safely used for those cases for which existing techniques fail. We illustrate the advantages of proposed method on a few benchmarks.
Starting Page	195
Ending Page	198
File Size	516069
Page Count	4
File Format	PDF
ISBN	9781467325394
e-ISBN	9781467325387
e-ISBN	9781467325370
DOI	10.1109/EPEPS.2012.6457875
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Vectors Convergence Convex functions Scattering Standards Fitting
Content Type	Text
Resource Type	Article

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