Quadrotor stabilization by Fuzzy Kalman filter

L. A. Paramo; E. C. Garcia; J. A. Meda; J. D.J. Rubio; J. O. Escobedo; R. Tapia; J. O. Hernandez; G. Lopez; J. F. Novoa; A. Aguilar

doi:10.3233/JIFS-191251

Quadrotor stabilization by Fuzzy Kalman filter

L. A. Paramo, E. C. Garcia, J. A. Meda, J. D.J. Rubio, J. O. Escobedo, R. Tapia, J. O. Hernandez, G. Lopez, J. F. Novoa, A. Aguilar

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

In this work, state vector estimation by means of the Fuzzy Kalman Filter (FKF) is used to generate a control signal that stabilizes an unmanned quadrotor aircraft. The framework for fuzzy Kalman Filter methodology has been successfully developed, and in this sense, the FKF is implemented and compared with Kalman Filter (KF) and extended Kalman Filter (EKF). It will be proved that the fuzzy version gives some advantages such as a smaller processing time and a smaller Mean Squared Error (MSE). Finally, these results are shown in graphics and tables.

Original language	English
Pages (from-to)	4485-4494
Number of pages	10
Journal	Journal of Intelligent and Fuzzy Systems
Volume	38
Issue number	4
DOIs	https://doi.org/10.3233/JIFS-191251
State	Published - 2020

Keywords

Control systems
Fuzzy systems stabilization
Kalman filtering
Quadrotor

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10.3233/JIFS-191251

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title = "Quadrotor stabilization by Fuzzy Kalman filter",

abstract = "In this work, state vector estimation by means of the Fuzzy Kalman Filter (FKF) is used to generate a control signal that stabilizes an unmanned quadrotor aircraft. The framework for fuzzy Kalman Filter methodology has been successfully developed, and in this sense, the FKF is implemented and compared with Kalman Filter (KF) and extended Kalman Filter (EKF). It will be proved that the fuzzy version gives some advantages such as a smaller processing time and a smaller Mean Squared Error (MSE). Finally, these results are shown in graphics and tables.",

keywords = "Control systems, Fuzzy systems stabilization, Kalman filtering, Quadrotor",

author = "Paramo, {L. A.} and Garcia, {E. C.} and Meda, {J. A.} and Rubio, {J. D.J.} and Escobedo, {J. O.} and R. Tapia and Hernandez, {J. O.} and G. Lopez and Novoa, {J. F.} and A. Aguilar",

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T1 - Quadrotor stabilization by Fuzzy Kalman filter

AU - Paramo, L. A.

AU - Garcia, E. C.

AU - Meda, J. A.

AU - Rubio, J. D.J.

AU - Escobedo, J. O.

AU - Tapia, R.

AU - Hernandez, J. O.

AU - Lopez, G.

AU - Novoa, J. F.

AU - Aguilar, A.

PY - 2020

Y1 - 2020

N2 - In this work, state vector estimation by means of the Fuzzy Kalman Filter (FKF) is used to generate a control signal that stabilizes an unmanned quadrotor aircraft. The framework for fuzzy Kalman Filter methodology has been successfully developed, and in this sense, the FKF is implemented and compared with Kalman Filter (KF) and extended Kalman Filter (EKF). It will be proved that the fuzzy version gives some advantages such as a smaller processing time and a smaller Mean Squared Error (MSE). Finally, these results are shown in graphics and tables.

AB - In this work, state vector estimation by means of the Fuzzy Kalman Filter (FKF) is used to generate a control signal that stabilizes an unmanned quadrotor aircraft. The framework for fuzzy Kalman Filter methodology has been successfully developed, and in this sense, the FKF is implemented and compared with Kalman Filter (KF) and extended Kalman Filter (EKF). It will be proved that the fuzzy version gives some advantages such as a smaller processing time and a smaller Mean Squared Error (MSE). Finally, these results are shown in graphics and tables.

KW - Control systems

KW - Fuzzy systems stabilization

KW - Kalman filtering

KW - Quadrotor

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U2 - 10.3233/JIFS-191251

DO - 10.3233/JIFS-191251

M3 - Artículo

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SN - 1064-1246

VL - 38

SP - 4485

EP - 4494

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

IS - 4

ER -

Quadrotor stabilization by Fuzzy Kalman filter

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