Minimum variance unbiased observer of a continuous LPV system with unknown input

Luc Meyer 1 Dalil Ichalal 1 Vincent Vigneron 1 Claire Vasiljevic 1
1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
Abstract : That paper addresses the problem of estimating the state and the unknown input in a continuous-time Linear Parameter Varying (LPV) system, in the presence of Gaussian white noises affecting both state and measurement equations. This estimation is made via the use of a Minimum Variance Unbiased Observer (as in Kalman Filtering). After recalling some useful results, the observer equations for state and unknown input estimation are established, and then an example is given in order to illustrate the talk.
Keywords : Unknown Input Observer Minimum VarianceObserver LPV Systems Continuous Systems Kalman Filter Dynamical System
Document type :
Conference papers
Complete list of metadatas
https://hal.archives-ouvertes.fr/hal-01695176
Contributor : Frédéric Davesne <>
Submitted on : Monday, January 29, 2018 - 10:12:21 AM
Last modification on : Monday, October 28, 2019 - 10:50:22 AM

Identifiers

Collections

UNIV-EVRY | IBISC | IBISC-SIBI | TDS-MACS | UNIV-PARIS-SACLAY

Citation

Luc Meyer, Dalil Ichalal, Vincent Vigneron, Claire Vasiljevic. Minimum variance unbiased observer of a continuous LPV system with unknown input. 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC 2017), Oct 2017, Banff, Canada. pp.2603--2606, ⟨10.1109/SMC.2017.8123017⟩. ⟨hal-01695176⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

115

Contact

Informations

CCSD