Closed-loop persistent identification of linear systems with unmodeled dynamics and stochastic disturbances
type: Item
instance of: scholarly article
title: Closed-loop persistent identification of linear systems with unmodeled dynamics and stochastic disturbances
zbMATH Open document ID: 1008.93027
DOI: 10.1016/S0005-1098(02)00054-7
author: Li Yi Wang
author: G. Yin
published in: Automatica
publication date: 2002-09-05T00:00:00Z
review text: The authors study the essential issues of time complexity and probing signal selection for persistent identification of a linear time-invariant system in a closed-loop setting. Certain basic properties of the identification mapping are established by setting up both upper and lower bounds on identification accuracy as functions of the length of observation, size of unmodeled dynamics, and stochastic disturbances. The inherent impact of unmodeled dynamics on identification accuracy and the benefits of reducing time complexity by stochastic averaging of disturbances are demonstrated. Periodic and full rank inputs are shown to process generic feedback-invariant features that are desirable for persistent closed-loop identification. It is shown that the mixed formulation, in which deterministic uncertainty of system dynamics is blended with random disturbances, is beneficial to the reduction of identification complexity.
reviewed by: Tzvetan Semerdjiev
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93C73
zbMATH DE Number: 1797129
zbMATH Keywords: system identification
zbMATH Keywords: closed-loop system
zbMATH Keywords: unmodeled dynamics
zbMATH Keywords: time complexity
zbMATH Keywords: length of observation
zbMATH Keywords: stochastic disturbances
zbMATH Keywords: accuracy
zbMATH Keywords: averaging
MaRDI profile type: Publication
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full work available at URL: s0005-1098(02)00054-7
OpenAlex ID: W2138959605
description: scientific article; zbMATH DE number 1797129