Implementation of Kalman filter algorithm on reduced models using Linear Matrix Inequality method and its application to measurement water level

Nenik Estuningsih, Fatmawati, Erna Apriliani

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper presents the model reduction and estimation of the state variables of the water level system using the Linear Matrix Inequality (LMI) method and the Kalman filter algorithm. We assume the system is asymptotic stable, controllable and observable, then we reduce it by LMI method. The reduced system obtained is a system that remains asymptotic stable, controllable, and observable. The reduction error using LMI method is smaller than the reduction error using Balanced Truncated (BT) method and Singular Perturbation Approximation (SPA) method. Next, we implemented the Kalman filter algorithm in the original system and the system was reduced by LMI method.

Original languageEnglish
Article number012054
JournalJournal of Physics: Conference Series
Volume1490
Issue number1
DOIs
Publication statusPublished - 9 Jun 2020
Event5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019 - Surabaya, Indonesia
Duration: 19 Oct 2019 → …

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