Open AccessImplementation of Kalman filter algorithm on reduced models using Linear Matrix Inequality method and its application to measurement water level
Author(s) -
Nenik Estuningsih,
Fatmawati Fatmawati,
Erna Apriliani
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1490/1/012054
Subject(s) - linear matrix inequality , kalman filter , control theory (sociology) , observable , mathematics , reduction (mathematics) , invariant extended kalman filter , perturbation (astronomy) , linear system , matrix (chemical analysis) , extended kalman filter , mathematical optimization , algorithm , computer science , statistics , mathematical analysis , artificial intelligence , physics , geometry , control (management) , materials science , quantum mechanics , composite material
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.