Passive macromodeling : theory and applications / Stefano Grivet-Talocia, Politecnico di Torino, Italy, Bjørn Gustavsen, SINTEF Energy Research, Norway.

Format:

Book

Author/Creator:

Grivet-Talocia, Stefano, 1970- author.

Gustavsen, Bjørn, 1965- author.

Series:

Wiley series in microwave and optical engineering.

Wiley Series in Microwave and Optical Engineering

Language:

English

Subjects (All):

Electromagnetic interference--Computer simulation.

Electromagnetic interference.

Analog electronic systems--Computer simulation.

Analog electronic systems.

Electric power systems--Computer simulation.

Electric power systems.

Passive components--Computer simulation.

Passive components.

Physical Description:

1 online resource (903 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2016.

Language Note:

English

Summary:

"In the first comprehensive treatment of passive macromodeling on the market, macromodeling experts Stefano Grivet-Talocia and Bjorn Gustavsen address the complex subject with examples of effective, proven methods. Finally, students and researchers may turn to a text that tends to the theoretical background essential to comprehending the algorithms' advantages and disadvantages. With the latest information on black-box passive macromodeling and software implementation, this book is a foolproof guide to both the basics and complexities of passive macromodeling"-- Provided by publisher.

"This book offers coverage of developments in linear macromodeling, with a focus on effective, proven methods. After starting with a definition of the fundamental properties that must characterize models of physical systems, the authors discuss several prominent passive macromodeling algorithms for lumped and distributed systems and compare them under accuracy, efficiency, and robustness standpoints. The book includes chapters with standard background material (such as linear time-invariant circuits and systems, basic discretization of field equations, state-space systems), as well as appendices collecting basic facts from linear algebra, optimization templates, and signals and transforms. The text also covers more technical and advanced topics, intended for the specialist, which may be skipped at first reading"-- Provided by publisher.

Contents:

Cover

Title Page

Copyright

Dedication

Contents

Preface

Chapter 1 Introduction

1.1 Why Macromodeling?

1.2 Scope

1.3 Macromodeling Flows

1.3.1 Macromodeling via Model Order Reduction

1.3.2 Macromodeling from Field Solver Data

1.3.3 Macromodeling from Measured Responses

1.4 Rational Macromodeling

1.5 Physical Consistency Requirements

1.6 Time-Domain Implementation

1.7 An Example

1.8 What Can Go Wrong?

Chapter 2 Linear Time-Invariant Circuits and Systems

2.1 Basic Definitions

2.1.1 Linearity

2.1.2 Memory and Causality

2.1.3 Time Invariance

2.1.4 Stability

2.1.5 Passivity

2.2 Linear Time-Invariant Systems

2.2.1 Impulse Response

2.2.2 Properties of LTI Systems

2.3 Frequency-Domain Characterizations

2.4 Laplace and Fourier Transforms

2.4.1 Bilateral Laplace Transform and Transfer Matrices

2.4.2 Causal LTI Systems and the Unilateral Laplace Transform

2.4.3 Fourier Transform

2.5 Signal and System Norms*

2.5.1 Signal Norms

2.5.2 System Norms

2.6 Multiport Representations

2.6.1 Ports and Terminals

2.6.2 Immittance Representations

2.6.3 Scattering Representations

2.6.4 Reciprocity

2.7 Passivity

2.7.1 Power and Energy

2.7.2 Passivity and Causality

2.7.3 The Static Case

2.7.4 The Dynamic Case

2.7.5 Positive Realness, Bounded Realness, and Passivity

2.7.6 Some Examples

2.8 Stability and Causality

2.8.1 Laplace-Domain Conditions for Causality

2.8.2 Laplace-Domain Conditions for BIBO Stability

2.8.3 Causality and Stability

2.9 Boundary Values and Dispersion Relations*

2.9.1 Assumptions

2.9.2 Reconstruction of H(s) for s ∈ C+

2.9.3 Reconstruction of H(s) for s ∈ jR

2.9.4 Causality and Dispersion Relations

2.9.5 Generalizations

2.10 Passivity Conditions on the Imaginary Axis*

Problems.

Chapter 3 Lumped LTI Systems

3.1 An Example from Circuit Theory

3.1.1 Variation on a Theme

3.1.2 Driving-Point Impedance

3.2 State-Space and Descriptor Forms

3.2.1 Singular Descriptor Forms

3.2.2 Internal Representations of Lumped LTI Systems

3.3 The Zero-Input Response

3.4 Internal Stability

3.4.1 Lyapunov Stability

3.4.2 Internal Stability of LTI Systems

3.5 The Lyapunov Equation

3.6 The Zero-State Response

3.6.1 Impulse Response

3.7 Operations on State-Space Systems

3.7.1 Interconnections

3.7.2 Inversion

3.7.3 Similarity Transformations

3.8 Gramians

3.8.1 Observability

3.8.2 Controllability

3.8.3 Minimal Realizations

3.9 Reciprocal State-Space Systems

3.10 Norms

3.10.1 L2 Norm

3.10.2 H∞ Norm

Problems

Chapter 4 Distributed LTI Systems

4.1 One-Dimensional Distributed Circuits

4.1.1 The Discrete-Space Case

4.1.2 The Continuous-Space Case

4.1.3 Discussion

4.2 Two-Dimensional Distributed Circuits*

4.2.1 The Discrete-Space Case

4.2.2 The Continuous-Space Case

4.2.3 A Closed-Form Solution

4.2.4 Spatial Discretization

4.2.5 Discussion

4.3 General Electromagnetic Characterization

4.3.1 3D Electromagnetic Modeling

4.3.2 Summary and Outlook

Chapter 5 Macromodeling Via Model Order Reduction

5.1 Model Order Reduction

5.2 Moment Matching

5.2.1 Moments

5.2.2 Padé Approximation and AWE

5.2.3 Complex Frequency Hopping

5.3 Reduction by Projection

5.3.1 Krylov Subspaces

5.3.2 Implicit Moment Matching: The Orthogonal Case

5.3.3 The Arnoldi Process

5.3.4 PRIMA

5.3.5 Multipoint Moment Matching

5.3.6 An Example

5.3.7 Implicit Moment Matching: The Biorthogonal Case

5.3.8 Padé Via Lanczos (PVL)

5.4 Reduction by Truncation

5.4.1 Balancing

5.4.2 Balanced Truncation.

5.5 Advanced Model Order Reduction*

5.5.1 Passivity-Preserving Balanced Truncation

5.5.2 Balanced Truncation of Descriptor Systems

5.5.3 Reducing Large-Scale Systems

Chapter 6 Black-Box Macromodeling and Curve Fitting

6.1 Basic Curve Fitting

6.1.1 Linear Least Squares

6.1.2 Maximum Likelihood Estimation

6.1.3 Polynomial Fitting

6.2 Direct Rational Fitting

6.2.1 Polynomial Ratio Form

6.2.2 Pole-Zero Form

6.2.3 Partial Fraction Form

6.2.4 Partial Fraction Form with Fixed Poles

6.2.5 Nonlinear Least Squares

6.3 Linearization via Weighting

6.4 Asymptotic Pole-Zero Placement

6.5 ARMA Modeling

6.5.1 Modeling from Time-Domain Responses

6.5.2 Modeling from Frequency Domain Responses

6.5.3 Conversion of ARMA Models

6.6 Prony's Method

6.7 Subspace-Based Identification*

6.7.1 Discrete-Time State-Space Systems

6.7.2 Macromodeling from Impulse Response Samples

6.7.3 Macromodeling from Input-Output Samples

6.7.4 From Discrete-Time to Continuous-Time State-Space Models

6.7.5 Frequency-Domain Subspace Identification

6.7.6 Generalized Pencil-of-Function Methods

6.7.7 Examples

6.8 Loewner Matrix Interpolation*

6.8.1 The Scalar Case

6.8.2 The Multiport Case

Chapter 7 The Vector Fitting Algorithm

7.1 The Sanathanan-Koerner Iteration

7.1.1 The Steiglitz-McBride Iteration

7.2 The Generalized Sanathanan-Koerner Iteration

7.2.1 General Basis Functions

7.2.2 The Partial Fraction Basis

7.3 Frequency-Domain Vector Fitting

7.3.1 A Simple Model Transformation

7.3.2 Computing the New Poles

7.3.3 The Vector Fitting Iteration

7.3.4 From GSK to VF

7.4 Consistency And Convergence

7.4.1 Consistency

7.4.2 Convergence

7.4.3 Formal Convergence Analysis

7.5 Practical VF Implementation.

7.5.1 Causality, Stability, and Realness

7.5.2 Order Selection and Initialization

7.5.3 Improving Numerical Robustness

7.6 Relaxed Vector Fitting

7.6.1 Weight Normalization, Noise, and Convergence

7.6.2 Relaxed Vector Fitting

7.7 Tuning VF

7.7.1 Weighting and Error Control

7.7.2 High-Frequency Behavior

7.7.3 High-Frequency Constraints

7.7.4 DC Point Enforcement

7.7.5 Simultaneous Constraints

7.8 Time-Domain Vector Fitting

7.9 z-Domain Vector Fitting

7.10 Orthonormal Vector Fitting

7.10.1 Orthonormal Rational Basis Functions

7.10.2 The OVF Iteration

7.10.3 The OVF Pole Relocation Step

7.10.4 Finding Residues

7.11 Other Variants

7.11.1 Magnitude Vector Fitting

7.11.2 Vector Fitting with L1 Norm Minimization

7.11.3 Dealing with Higher Pole Multiplicities

7.11.4 Including Higher Order Derivatives

7.11.5 Hard Relocation of Poles

7.12 Notes on Overfitting and Ill-Conditioning

7.12.1 Exact Model Identification

7.12.2 Curve Fitting

7.13 Application Examples

7.13.1 Surface Acoustic Wave Filter

7.13.2 Subnetwork Equivalent

7.13.3 Transformer Modeling from Time-Domain Measurements

Chapter 8 Advanced Vector Fitting for Multiport Problems

8.1 Introduction

8.2 Adapting VF to Multiple Responses

8.2.1 Pole Identification

8.2.2 Fast Vector Fitting

8.2.3 Residue Identification

8.3 Multiport Formulations

8.3.1 Single-Element Modeling: Multi-SISO Structure

8.3.2 Single-Column Modeling: Multi-SIMO Structure

8.3.3 Matrix Modeling: MIMO Structure

8.3.4 Matrix Modeling: Minimal Realizations

8.3.5 Sparsity Considerations

8.4 Enforcing Reciprocity

8.4.1 External Reciprocity

8.4.2 Internal Reciprocity*

8.5 Compressed Macromodeling

8.5.1 Data Compression

8.5.2 Compressed Rational Approximation.

8.5.3 An Application Example

8.6 Accuracy Considerations

8.6.1 Noninteracting Models

8.6.2 Interacting Models, Scalar Case

8.6.3 Error Magnification in Multiport Systems

8.7 Overcoming Error Magnification

8.7.1 Elementwise Inverse Weighting

8.7.2 Diagonalization

8.7.3 Mode-Revealing Transformations

8.7.4 Modal Vector Fitting

8.7.5 External and Internal Ports

Chapter 9 Passivity Characterization of Lumped LTI Systems

9.1 Internal Characterization of Passivity

9.1.1 A First Order Example

9.1.2 The Dissipation Inequality

9.1.3 Lumped LTI Systems

9.2 Passivity of Lumped Immittance Systems

9.2.1 Rational Positive Real Matrices

9.2.2 Extracting Purely Imaginary Poles

9.2.3 The Positive Real Lemma

9.2.4 Positive Real Functions Revisited

9.2.5 Popov Functions and Spectral Factorizations

9.2.6 Hamiltonian Matrices

9.2.7 Passivity Characterization via Hamiltonian Matrices

9.2.8 Determination of Local Passivity Violations

9.2.9 Quantification of Passivity Violations via Bisection

9.2.10 Quantification of Passivity Violations via Sampling

9.2.11 Frequency Transformations

9.2.12 Extended Hamiltonian Pencils

9.2.13 Generalized Hamiltonian Pencils

9.2.14 Positive Real Lemma for Descriptor Systems

9.3 Passivity of Lumped Scattering Systems

9.3.1 Rational Bounded Real Matrices

9.3.2 The Bounded Real Lemma

9.3.3 Bounded Real Functions Revisited

9.3.4 Popov Functions, Spectral Factorizations, and Hamiltonian Matrices

9.3.5 Passivity Characterization via Hamiltonian Matrices

9.3.6 Determination of Local Passivity Violations

9.3.7 Quantification of Passivity Violations via Bisection

9.3.8 Quantification of Passivity Violations via Sampling

9.3.9 Extended Hamiltonian Pencils

9.3.10 Generalized Hamiltonian Pencils.

9.3.11 Bounded Real Lemma for Descriptor Systems.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-119-14095-1

1-119-14097-8

OCLC:

927509568