Title
Enforcing Passivity of Parameterized LTI Macromodels via Hamiltonian-Driven Multivariate Adaptive Sampling
Abstract
We present an algorithm for passivity verification and enforcement of multivariate macromodels whose state-space matrices depend in closed form on a set of external or design parameters. Uniform passivity throughout the parameter space is a fundamental requirement of parameterized macromodels of physically passive structures, that must be guaranteed during model generation. Otherwise, numerical instabilities may occur, due to the ability of nonpassive models to generate energy. In this paper, we propose the first available algorithm that, starting from a generic parameter-dependent state-space model, identifies the regions in the frequency-parameter space where the model behaves locally as a nonpassive system. The approach we pursue is based on an adaptive sampling scheme in the parameter space, which iteratively constructs and perturbs the eigenvalue spectrum of suitable skew-Hamiltonian/Hamiltonian pencils, with the objective of identifying the regions where some of these eigenvalues become purely imaginary, thus pinpointing local passivity violations. The proposed scheme is able to detect all relevant violations. An outer iterative perturbation method is then applied to the model coefficients in order to remove such violations and achieve uniform passivity. Although a formal proof of global convergence is not available, the effectiveness of the proposed implementation of the passivity verification and enforcement schemes is demonstrated on several examples.
Year
DOI
Venue
2020
10.1109/TCAD.2018.2883962
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Keywords
Field
DocType
Numerical models,Solid modeling,Mathematical model,Eigenvalues and eigenfunctions,Indexes,Scattering,Reliability
Passivity,Parameterized complexity,Hamiltonian (quantum mechanics),Multivariate statistics,Adaptive sampling,Computer science,Control theory,Electronic engineering
Journal
Volume
Issue
ISSN
39
1
0278-0070
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Alessandro Zanco100.68
Stefano Grivet-Talocia222.61
Tommaso Bradde300.68
Stefano De Marco448.26