GSVD Information Filter for Discrete-Time Linear Dynamic Systems with Gross Errors

Gbolahan P. Dada, Antonios Armaou

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


Development of accurate state estimation with observer models from process sensor measurements are often limited by noisy measurements typically resulting from sensor fidelity, process disturbances and variables correlations. The estimation of state variables of dynamic systems with noisy output measurements, are traditionally modelled with Gaussian white noise. Noisy measurements of industrial dynamic processes are expressed as gross error additions to bounded expected sensor measurements. This noise treatment targets the design of filters using a combination of GSVD factorization of error covariance and gross error identification. The resulting output measurement model is illustrated on the simplified Tennessee Eastman Process application, where it is successfully applied for accurate state estimation.

Original languageEnglish (US)
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665441971
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
ISSN (Print)0743-1619


Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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