Model Validation in l1 Using Frequency-Domain Data

Wenguo Liu; Jie Chen

doi:10.1109/CDC.2003.1272399

Model Validation in l₁ Using Frequency-Domain Data

Wenguo Liu, Jie Chen^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In this paper we study the problem of invalidating uncertain models with an additive uncertainty. The problem is to check the existence of an uncertainty and a measurement noise which fit to the given model structure and the uncertainty/noise description, as well as the experimental data used for invalidation. We consider a mixed setting in which the uncertainty is characterized in time domain by the l₁ induced system norm, while the available data are frequency response samples of the system. We show that this problem, which by formulation poses an infinite-dimensional primal optimization problem, can be solved in a dual, finite-dimensional space with finitely many constraints.

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Decision and Control
Pages	6509-6514
Volume	6
DOIs	https://doi.org/10.1109/CDC.2003.1272399
Publication status	Published - 2003
Externally published	Yes
Event	42nd IEEE Conference on Decision and Control, CDC 2003: CDC 2003 - Maui, United States Duration: 9 Dec 2003 → 12 Dec 2003

Publication series

Name
ISSN (Print)	0191-2216

Conference

Conference	42nd IEEE Conference on Decision and Control, CDC 2003
Place	United States
City	Maui
Period	9/12/03 → 12/12/03

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10.1109/CDC.2003.1272399

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Cite this

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title = "Model Validation in l1 Using Frequency-Domain Data",

abstract = "In this paper we study the problem of invalidating uncertain models with an additive uncertainty. The problem is to check the existence of an uncertainty and a measurement noise which fit to the given model structure and the uncertainty/noise description, as well as the experimental data used for invalidation. We consider a mixed setting in which the uncertainty is characterized in time domain by the l1 induced system norm, while the available data are frequency response samples of the system. We show that this problem, which by formulation poses an infinite-dimensional primal optimization problem, can be solved in a dual, finite-dimensional space with finitely many constraints.",

author = "Wenguo Liu and Jie Chen",

year = "2003",

doi = "10.1109/CDC.2003.1272399",

language = "English",

isbn = "0780379241",

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AB - In this paper we study the problem of invalidating uncertain models with an additive uncertainty. The problem is to check the existence of an uncertainty and a measurement noise which fit to the given model structure and the uncertainty/noise description, as well as the experimental data used for invalidation. We consider a mixed setting in which the uncertainty is characterized in time domain by the l1 induced system norm, while the available data are frequency response samples of the system. We show that this problem, which by formulation poses an infinite-dimensional primal optimization problem, can be solved in a dual, finite-dimensional space with finitely many constraints.

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M3 - RGC 32 - Refereed conference paper (with host publication)

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BT - Proceedings of the IEEE Conference on Decision and Control

T2 - 42nd IEEE Conference on Decision and Control, CDC 2003

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