Fuzzy Kalman Filter using Linear Matrix Inequalities

Hanna Aboukheir; Marco Herrera; Edinzo Iglesias; Oscar Camacho

doi:10.1109/CHILECON54041.2021.9703045

Fuzzy Kalman Filter using Linear Matrix Inequalities

Hanna Aboukheir, Marco Herrera, Edinzo Iglesias, Oscar Camacho

Ingeniería Electrónica y Automatización

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The Kalman filter has been extensively used in different applications due to its strengths in estimating the system states under noisy observations. In this paper, a modification of the classical Kalman filter for nonlinear state estimation is presented; firstly, a polytopic set of linear discrete-time models based on a Takagi-Sugeno inference system is used to describe the nonlinear operating region. The stabilizing gains of the linear filters are calculated using Linear Matrix Inequalities (LMI), the proposal is evaluated through simulations.

Original language	English
Title of host publication	2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665408738
DOIs	https://doi.org/10.1109/CHILECON54041.2021.9703045
State	Published - 2021
Event	2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 - Virtual, Online, Chile Duration: 6 Dec 2021 → 9 Dec 2021

Publication series

Name	2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

Conference

Conference	2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Country/Territory	Chile
City	Virtual, Online
Period	6/12/21 → 9/12/21

Keywords

Fuzzy Systems
Kalman Filtering
Linear Matrix Inequalities

Access to Document

10.1109/CHILECON54041.2021.9703045

Cite this

Aboukheir, H., Herrera, M., Iglesias, E., & Camacho, O. (2021). Fuzzy Kalman Filter using Linear Matrix Inequalities. In 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 (2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CHILECON54041.2021.9703045

Aboukheir, Hanna ; Herrera, Marco ; Iglesias, Edinzo et al. / Fuzzy Kalman Filter using Linear Matrix Inequalities. 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021).

@inproceedings{4fe72249e115444db39c5d01cbcb81ef,

title = "Fuzzy Kalman Filter using Linear Matrix Inequalities",

abstract = "The Kalman filter has been extensively used in different applications due to its strengths in estimating the system states under noisy observations. In this paper, a modification of the classical Kalman filter for nonlinear state estimation is presented; firstly, a polytopic set of linear discrete-time models based on a Takagi-Sugeno inference system is used to describe the nonlinear operating region. The stabilizing gains of the linear filters are calculated using Linear Matrix Inequalities (LMI), the proposal is evaluated through simulations.",

keywords = "Fuzzy Systems, Kalman Filtering, Linear Matrix Inequalities",

author = "Hanna Aboukheir and Marco Herrera and Edinzo Iglesias and Oscar Camacho",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021 ; Conference date: 06-12-2021 Through 09-12-2021",

year = "2021",

doi = "10.1109/CHILECON54041.2021.9703045",

language = "Ingl{\'e}s",

series = "2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021",

}

Aboukheir, H, Herrera, M, Iglesias, E & Camacho, O 2021, Fuzzy Kalman Filter using Linear Matrix Inequalities. in 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021. 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021, Virtual, Online, Chile, 6/12/21. https://doi.org/10.1109/CHILECON54041.2021.9703045

Fuzzy Kalman Filter using Linear Matrix Inequalities. / Aboukheir, Hanna; Herrera, Marco; Iglesias, Edinzo et al.
2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fuzzy Kalman Filter using Linear Matrix Inequalities

AU - Aboukheir, Hanna

AU - Herrera, Marco

AU - Iglesias, Edinzo

AU - Camacho, Oscar

PY - 2021

Y1 - 2021

N2 - The Kalman filter has been extensively used in different applications due to its strengths in estimating the system states under noisy observations. In this paper, a modification of the classical Kalman filter for nonlinear state estimation is presented; firstly, a polytopic set of linear discrete-time models based on a Takagi-Sugeno inference system is used to describe the nonlinear operating region. The stabilizing gains of the linear filters are calculated using Linear Matrix Inequalities (LMI), the proposal is evaluated through simulations.

AB - The Kalman filter has been extensively used in different applications due to its strengths in estimating the system states under noisy observations. In this paper, a modification of the classical Kalman filter for nonlinear state estimation is presented; firstly, a polytopic set of linear discrete-time models based on a Takagi-Sugeno inference system is used to describe the nonlinear operating region. The stabilizing gains of the linear filters are calculated using Linear Matrix Inequalities (LMI), the proposal is evaluated through simulations.

KW - Fuzzy Systems

KW - Kalman Filtering

KW - Linear Matrix Inequalities

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U2 - 10.1109/CHILECON54041.2021.9703045

DO - 10.1109/CHILECON54041.2021.9703045

M3 - Contribución a la conferencia

AN - SCOPUS:85126916404

T3 - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

BT - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021

Y2 - 6 December 2021 through 9 December 2021

ER -

Aboukheir H, Herrera M, Iglesias E, Camacho O. Fuzzy Kalman Filter using Linear Matrix Inequalities. In 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021). doi: 10.1109/CHILECON54041.2021.9703045

Fuzzy Kalman Filter using Linear Matrix Inequalities

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