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Communication Dans Un Congrès Année : 2000

Finite memory observer for input-output estimation. Application to data reconciliation and diagnosis

Résumé

We are concerned here with dynamical processes whose states and inputs are measured or partially measured. Due to the noise which corrupt the measurements, we want to simultaneously estimate the states and the inputs. For the case where all the measurements are available, the estimation may be considered as a filtering technique; otherwise, the problem is more complicated as we want also to obtain an estimation of the unmeasured variables. In the very particular but difficult situation where not any inputs is measured, the problem may be linked to those involving unknown input observers; in signal processing, when the system is time-invariant, this problem is often referred to as deconvolution. The proposed technique is applioed for measurements corrupted by noise which generally gives bad estimations of the unknown inputs. In order to obtain smooth estimations, additional constraints are proposed.
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Dates et versions

hal-00315743 , version 1 (05-08-2015)

Identifiants

  • HAL Id : hal-00315743 , version 1

Citer

José Ragot, Frédéric Kratz, Didier Maquin. Finite memory observer for input-output estimation. Application to data reconciliation and diagnosis. IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS'2000, Jun 2000, Budapest, Hungary. pp.587-592. ⟨hal-00315743⟩
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