In this paper, a stochastic modeling approach is proposed for time-domain variability analysis of general linear and passive systems with uncertain parameters. Starting from the polynomial chaos (PC) expansion of the scattering parameters, the Galerkin projections (GP) method is adopted to build an augmented scattering matrix which describes the relationship between the corresponding PC coefficients of the input and output port signals. The Vector Fitting (VF) algorithm is then used to obtain a stable and passive state-space model of such augmented matrix. As a result, a stochastic system is described by an equivalent deterministic macro model and the time-domain variability analysis can be performed by means of one time-domain simulation. The feasibility, efficiency and accuracy of the proposed technique are verified by comparison with conventional Monte Carlo (MC) approach for a suitable numerical example.

Macromodeling of general linear systems under stochastic variations

Antonini G.
2017

Abstract

In this paper, a stochastic modeling approach is proposed for time-domain variability analysis of general linear and passive systems with uncertain parameters. Starting from the polynomial chaos (PC) expansion of the scattering parameters, the Galerkin projections (GP) method is adopted to build an augmented scattering matrix which describes the relationship between the corresponding PC coefficients of the input and output port signals. The Vector Fitting (VF) algorithm is then used to obtain a stable and passive state-space model of such augmented matrix. As a result, a stochastic system is described by an equivalent deterministic macro model and the time-domain variability analysis can be performed by means of one time-domain simulation. The feasibility, efficiency and accuracy of the proposed technique are verified by comparison with conventional Monte Carlo (MC) approach for a suitable numerical example.
978-1-5090-6185-3
File in questo prodotto:
File Dimensione Formato  
Macromodeling of General Linear Systems Under Stochastic Variations.pdf

solo utenti autorizzati

Tipologia: Documento in Pre-print
Licenza: Creative commons
Dimensione 306.92 kB
Formato Adobe PDF
306.92 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/175682
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 1
social impact