State and parameter estimation of nonlinear systems: a multi-observer approach

Michelle S. Chong, Dragan Nesic, Romain Postoyan, Levin Kuhlmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


We present a multi-observer approach for the parameter and state estimation of continuous-time nonlinear systems. For nominal parameter values in the known parameter set, state observers are designed with a robustness property. At any time instant, one observer is selected by a given criterion to provide its state estimate and its corresponding nominal parameter value. Provided that a persistency of excitation condition holds, we guarantee the convergence of state and parameter estimates up to a given margin of error which can be reduced by increasing the number of observers. The potential computational burden of the scheme is eased by introducing a dynamic parameter re-sampling technique, where the nominal parameter values are iteratively updated using a zoom-in procedure on the parameter set. We illustrate the efficacy of the algorithm on a model of neural dynamics.

Original languageEnglish
Title of host publication53rd IEEE Conference on Decision and Control
Subtitle of host publicationDecember 15-17, 2014. Los Angeles, California, USA
EditorsAndrew R. Teel
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781467360906, 9781479977451
ISBN (Print)9781479977468
Publication statusPublished - 2014
Externally publishedYes
EventIEEE Conference on Decision and Control 2014 - J.W. Marriott Hotel, Los Angeles, United States of America
Duration: 15 Dec 201417 Dec 2014
Conference number: 53rd (Proceedings)


ConferenceIEEE Conference on Decision and Control 2014
Abbreviated titleCDC 2014
Country/TerritoryUnited States of America
CityLos Angeles
Internet address

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