Confidence Bounds on Identification of Linear Systems with Multiplicative Noise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Linear systems with multiplicative noise (LSMN) generalize the more common case of additive noise models. The multiplicative noise can model state-dependent noise and variations in the dynamics. We present an LSMN system identification algorithm which estimates both the first and second moments of the system parameters, and offers a probability bound on the estimates. We further develop an online scheme for identification and a robust control scheme based on the estimation bounds. Numerical examples are provided.

Original languageEnglish (US)
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2212-2217
Number of pages6
ISBN (Electronic)9781665441971
DOIs
StatePublished - May 25 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: May 25 2021May 28 2021

Publication series

NameProceedings of the American Control Conference
Volume2021-May
ISSN (Print)0743-1619

Conference

Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Period5/25/215/28/21

Bibliographical note

Publisher Copyright:
© 2021 American Automatic Control Council.

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