Foundations and Trends® in Systems and Control > Vol 4 > Issue 3-4

Nonlinear Model Reduction by Moment Matching

Giordano Scarciotti, Imperial College London, UK, g.scarciotti@imperial.ac.uk Alessandro Astolfi, Imperial College London and Universita di Roma “Tor Vergata”, UK and Italy, a.astolfi@imperial.ac.uk
 
Suggested Citation
Giordano Scarciotti and Alessandro Astolfi (2017), "Nonlinear Model Reduction by Moment Matching", Foundations and Trends® in Systems and Control: Vol. 4: No. 3-4, pp 224-409. http://dx.doi.org/10.1561/2600000012

Published: 08 Aug 2017
© 2017 G. Scarciotti and A. Astolfi
 
Subjects
Systems Theory,  Control of Nonlinear Systems,  Control of Delay Systems,  Filtering, Estimation, Identification
 
Keywords
Model Order ReductionPhasors
 

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In this article:
1. Introduction
2. Model reduction by moment matching
3. Model reduction of time-delay systems
4. Data-driven model reduction
5. Model reduction with explicit signal generators
6. Conclusions
References

Abstract

Mathematical models are at the core of modern science and technology. An accurate description of behaviors, systems and processes often requires the use of complex models which are difficult to analyze and control. To facilitate analysis of and design for complex systems, model reduction theory and tools allow determining “simpler” models which preserve some of the features of the underlying complex description. A large variety of techniques, which can be distinguished depending on the features which are preserved in the reduction process, has been proposed to achieve this goal. One such a method is the moment matching approach.

This monograph focuses on the problem of model reduction by moment matching for nonlinear systems. The central idea of the method is the preservation, for a prescribed class of inputs and under some technical assumptions, of the steady-state output response of the system to be reduced. We present the moment matching approach from this vantage point, covering the problems of model reduction for nonlinear systems, nonlinear time-delay systems, data-driven model reduction for nonlinear systems and model reduction for “discontinuous” input signals. Throughout the monograph linear systems, with their simple structure and strong properties, are used as a paradigm to facilitate understanding of the theory and provide foundation of the terminology and notation. The text is enriched by several numerical examples, physically motivated examples and with connections to well-established notions and tools, such as the phasor transform.

DOI:10.1561/2600000012
ISBN: 978-1-68083-330-0
200 pp. $99.00
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ISBN: 978-1-68083-331-7
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Table of contents:
1. Introduction
2. Model reduction by moment matching
3. Model reduction of time-delay systems
4. Data-driven model reduction
5. Model reduction with explicit signal generators
6. Conclusions
References

Nonlinear Model Reduction by Moment Matching

Reduced order models, or model reduction, have been used in many technologically advanced areas to ensure the associated complicated mathematical models remain computable. For instance, reduced order models are used to simulate weather forecast models and in the design of very large scale integrated circuits and networked dynamical systems.

For linear systems, the model reduction problem has been addressed from several perspectives and a comprehensive theory exists. Although many results and efforts have been made, at present there is no complete theory of model reduction for nonlinear systems or, at least, not as complete as the theory developed for linear systems.

This monograph presents, in a uniform and complete fashion, moment matching techniques for nonlinear systems. This includes extensive sections on nonlinear time-delay systems; moment matching from input/output data and the limitations of the characterization of moment based on a signal generator described by differential equations. Each section is enriched with examples and is concluded with extensive bibliographical notes.

This monograph provides a comprehensive and accessible introduction into model reduction for researchers and students working on non-linear systems.

 
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