dor_id: 4110146

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100.1.#.a: Tamilselvan, G.M.; Aarthy, P.

524.#.#.a: Tamilselvan, G.M., et al. (2017). Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system. Journal of Applied Research and Technology; Vol. 15 Núm. 5. Recuperado de https://repositorio.unam.mx/contenidos/4110146

245.1.0.a: Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2017

264.#.1.c: 2019-06-14

653.#.#.a: Fuzzy logic controller; Proportional integral derivative controller; Kalman algorithm; Matlab

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

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041.#.7.h: eng

520.3.#.a: In a non-linear process like conical tank system, controlling the liquid level was carried out by proportional integral derivative (PID) controller. But then it does not provide an accurate result. So in order to obtain accurate and effective response, intelligence is added into the system by using fuzzy logic controller (FLC). FLC which helps in maintaining the liquid level in a conical tank has been developed and applied to various fields. The result acquired using FLC will be more precise when compared to PID controller. But FLC cannot adapt a wide range of working environments and also there is no systematic method to design the membership functions (MFs) for inputs and outputs of a fuzzy system. So an adaptive algorithm called Kalman algorithm which employs fuzzy logic rules is used to adapt the Kalman filter to accommodate changes in the system parameters. The Kalman algorithm which employs fuzzy logic rules adjust the controller parameters automatically during the operation process of a system and controller is used to reduce the error in noisy environments. This technique is applied in a conical tank system. Simulations and results show that this method is effective for using fuzzy controller.

773.1.#.t: Journal of Applied Research and Technology; Vol. 15 Núm. 5

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022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

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doi: https://doi.org/10.1016/j.jart.2017.05.004

harvesting_date: 2023-11-08 13:10:00.0

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file_creation_date: 2017-11-23 17:17:44.0

file_modification_date: 2017-11-23 11:55:58.0

file_creator: G.M. Tamilselvan

file_name: 52d51e83d227b4886ec9375cfe536387c0a37d70f23a341a3d800f186389036f.pdf

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last_modified: 2024-03-19 14:00:00

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Artículo

Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system

Tamilselvan, G.M.; Aarthy, P.

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Tamilselvan, G.M., et al. (2017). Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system. Journal of Applied Research and Technology; Vol. 15 Núm. 5. Recuperado de https://repositorio.unam.mx/contenidos/4110146

Descripción del recurso

Autor(es)
Tamilselvan, G.M.; Aarthy, P.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system
Fecha
2019-06-14
Resumen
In a non-linear process like conical tank system, controlling the liquid level was carried out by proportional integral derivative (PID) controller. But then it does not provide an accurate result. So in order to obtain accurate and effective response, intelligence is added into the system by using fuzzy logic controller (FLC). FLC which helps in maintaining the liquid level in a conical tank has been developed and applied to various fields. The result acquired using FLC will be more precise when compared to PID controller. But FLC cannot adapt a wide range of working environments and also there is no systematic method to design the membership functions (MFs) for inputs and outputs of a fuzzy system. So an adaptive algorithm called Kalman algorithm which employs fuzzy logic rules is used to adapt the Kalman filter to accommodate changes in the system parameters. The Kalman algorithm which employs fuzzy logic rules adjust the controller parameters automatically during the operation process of a system and controller is used to reduce the error in noisy environments. This technique is applied in a conical tank system. Simulations and results show that this method is effective for using fuzzy controller.
Tema
Fuzzy logic controller; Proportional integral derivative controller; Kalman algorithm; Matlab
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

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