Disturbance estimation using sliding mode for discrete Kalman filter

Jung Han Kim, Jun Ho Oh

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

Abstract

A novel approach to estimate disturbances using sliding mode for optimal Kalman filter is presented. We showed that the disturbance estimation problem can be converted into discrete tracking problem by using the difference of time update and measurement update of Kalman filter. Disturbances cannot be modeled or measured previously, so the robustness of sliding mode provides very effective tools for disturbance estimation. We developed and improved a disturbance estimation algorithm using discrete sliding mode for discrete Kalman filter. The suggested algorithm can be easily implemented on real time applications.

Original languageEnglish
Pages (from-to)1918-1919
Number of pages2
JournalProceedings of the IEEE Conference on Decision and Control
Volume2
StatePublished - 1998
EventProceedings of the 1998 37th IEEE Conference on Decision and Control (CDC) - Tampa, FL, USA
Duration: 16 Dec 199818 Dec 1998

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