Identification and estimation of state variables on reduced model using balanced truncation method

International Conference on Mathematics: Education, Theory and Application

数学;教育

Lesnussa, Trifena Punana^1 ; Arif, Didik Khusnul^1 ; Adzkiya, Dieky^1 ; Apriliani, Erna^1

Department of Mathematics, Institut Teknologi Sepuluh Nopember, Surabaya

60111, Indonesia^1

关键词: Balanced systems;    Balanced truncation method;    Estimation results;    Kalman filter algorithms;    Model order reduction;    Model reduction;    Original systems;    Reduced systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/855/1/012023/pdf
DOI  :  10.1088/1742-6596/855/1/012023

学科分类：发展心理学和教育心理学

来源: IOP

【 摘 要 】

In this paper, we study the identification of variables on a model reduction process and estimation of variables on reduced system. We aim to relate variables on reduced and original system, so that we can compare the estimation accuracy of the original system and reduced system. As such, the objective of this paper is to discuss identification and estimation of variables on reduced model. First, model order reduction is done by using balanced truncation method. This process begins with the construction of balanced system. After that, we identify the relationship between variables of the balanced system and the original system. Then, we eliminate variables of the balanced system that have a small influence on the system. Furthermore, we estimate state variables on the original system and reduced system using a Kalman Filter algorithm. Finally, we compare the estimation result of the identified reduced and original system.